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Full-RAG: A modern architecture for hyper-personalization

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Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.

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Full-RAG: A modern architecture for hyper-personalization Mike Del Balso CEO & Co-Founder
2 Goal: highly personalized travel recommendations We think you’ll love ?
How can we get a good suggestion from a model? 3
4 Fine tune? Improving model’s intrinsic knowledge How can we get a better recommendation? Prompt Engineer? Rewording the question, giving time to think RAG? Improving model’s knowledge about the current situation
5 Low quality recommendation content VectorDB Candidates: Cities Recommendation LLM Traditional RAG (Stone age) City: Paris Country: France City: Tokyo Country: France City: Johannesburg Country: South Africa 🙁 “Visit Paris!” Uncontextualized Candidates Query: Where should I travel over summer break?
6 Where should I travel over summer break? Stranger Travel Agent Low context, High expertise Paris. Your Best Friend Travel Agent High context, High Expertise You said you loved that sailing trip last summer, why not go check out the Rodos Cup in Greece? Rhodes has a super cool old town with lots of great little cafés.
7 Without context (i.e. in Trad RAG), we just have uncontextualized candidates Uncontextualized Candidate City: Paris Country: France City: Tokyo Country: Japan
Context is the relevant information that AI models use to understand a situation and make decisions. 8
9 Context enriches the candidate with more information to make it easier to reason about Uncontextualized Candidate Contextualized Candidate City: Paris Country: France Weather: 20°C, sunny Activities: Museums, cafes, river tours Nature: Fontainebleau, Versailles gardens Events: Fashion Week, Bastille Day Cuisine: Croissants, escargot Language: French Cost/Day: 200 USD Safety: High Visit Time: Apr-Jun, Sep-Nov Accessibility: High, extensive public transport Historic Sites: Eiffel Tower, Notre Dame Accommodation Range: Hostels to luxury hotels Visa Ease: Schengen Area, visa policies vary Nightlife: Vibrant, diverse options Family Friendly: Yes, many activities Art Scene: Louvre, Montmartre Shopping: Boutiques, flea markets Internet Access: High-speed, widely available City: Paris Country: France
City: Paris Country: France City: Paris Country: France Weather: 20°C, sunny Activities: Museums, cafes, river tours Nature: Fontainebleau, Versailles gardens Events: Fashion Week, Bastille Day Cuisine: Croissants, escargot Language: French Cost/Day: 200 USD Safety: High Visit Time: Apr-Jun, Sep-Nov Accessibility: High, extensive public transport Historic Sites: Eiffel Tower, Notre Dame Accommodation Range: Hostels to luxury hotels Visa Ease: Schengen Area, visa policies vary Nightlife: Vibrant, diverse options Family Friendly: Yes, many activities Art Scene: Louvre, Montmartre Shopping: Boutiques, flea markets Internet Access: High-speed, widely available 10 Personalized Context enriches candidates with user-level information Without context With context With Personalized Context City: Paris Country: France Weather: 20°C, sunny Activities: Museums, cafes, river tours Nature: Fontainebleau, Versailles gardens … Preferred Climate: Mild Interest in History: High Dining Preference: Gourmet/Fine dining Cultural Interest: High in arts and fashion Budget: Luxury Accommodation Preference: Boutique hotels Preferred Language: Prefers English-friendly destinations Activity Level: Moderate, enjoys leisurely strolls and seated activities Travel Experience: Seasoned traveler, prefers depth of experience Travel Group: Solo traveler Interest in Shopping: High, prefers unique boutiques Nightlife Interest: Low, prefers quiet evenings Interest in Local Cuisine: High, enjoys trying national dishes Interest in Events: Moderate, selectively attends major events Transportation Preference: Public transport, occasional taxi
11 Examples of context Destination Insights Cultural Significance Local customs, events, and holidays at the destination Safety and Alerts Current travel advisories and safety warnings Attractions and Activities Information on points of interest and things to do Lodging and Transport Availability and options for accommodation and local travel User-Centric Data Historical Interactions Including search history and previous bookings Demographic Information Age, language preferences, and other personal data Travel Patterns Data on past destinations and types of travel Preferences and Real-Time Data Activity Monitoring User's current engagement with the platform Active Input Immediate queries and filter settings Preference Settings Explicitly stated travel preferences and interests Situational Context Geographic Position The user’s current or selected location Temporal Context Time of day, date, and season Economic Context Financial Indicators User's budget range and previous spending habits Currency Trends Current exchange rates affecting travel costs External Influences Event Schedules Local events that could impact or enhance the travel experience Weather Patterns Forecasted weather conditions for the destination ... ... ...
No context High context Quality of response 12 Where should I travel over summer break?
13 VectorDB Candidates Recommendation LLM Create personalized context by enriching candidates with relevant user data City: Paris Country: France City: Tokyo Country: France City: Johannesburg Country: South Africa Feature Platform Candidate Data User Data Candidates w/ personalized context Best Friend-level Travel Agent recommendation “Kyoto is perfect for you because…” The Feature platform orchestrates context assembly
Frequency of city vs. countryside destinations Likelihood to engage in water sports Historical landmark visitation history Language proficiency for non-English destinations Desire for luxury vs. budget accommodations Appreciation for local music and performance arts Engagement with nature and wildlife conservation areas Interest in volunteer tourism opportunities Local public transportation efficiency Accessibility of medical facilities in the destination Economic stability of the destination country Political climate's impact on tourist safety Visa and entry requirements for the destination Current exchange rate advantages Local health advisories or travel restrictions Event timing, such as major sports or cultural events Availability of direct vs. connecting flights Seasonal tourist crowd levels Local peak dining times and availability Regional security advisories Cultural norms and attire expectations Time zone differences affecting activity planning Environmental sustainability initiatives at the destination Local telecommunications infrastructure for connectivity Historical weather patterns for planned travel dates Area-specific traveler reviews and ratings Local emergency services and language support Average local costs for tourists Destination-specific travel insurance recommendations Local customs clearance processes for travelers Recent developments in local tourism facilities Availability of multilingual guided tours Best High personalization → Better recommendations VectorDB Candidates Recommendation LLM City: Paris Country: France City: Tokyo Country: France City: Johannesburg Country: South Africa Destination Data Candidates w/ Context Feature Platform 14 User Data
We think you’ll love 15 Where to stay ?
Tonight: Sushi at Festival in Gion! 16 Last-Minute Opening: A few coveted spots at Chef Takumi Nishimura's 'Sushi Mastery' workshop have just opened up—right in the heart of Gion, a few minutes walk from you. Seize this rare chance to handcraft the praised dragonfly roll, adorned with top-choice sea urchin, as you've keenly blogged about. The forecast promises a perfect evening with clear skies to enjoy this gastronomic affair. The workshop has Dassai Umeshu 23 sake that you've been eager to try. Act now; these tickets won’t last! Where to stay First, we have the Hotel Mume located at 東山区新門前通梅本 町261. This amazing hotel has an outstanding average rating of 5.0 based on 8 reviews. Book now Following closely is Shiraume at 東山区祗園新橋白川筋 . Also boasting an average rating of 5.0 from 12 reviews, it's highly recommended and beloved by previous travelers. Book now Lastly, we have the SUIRAN LUXURY COLLECTION HOTEL KYOTO located at 右京区嵯峨 天 龍寺芒ノ馬場町 12. This luxurious hotel in Kyoto also got an average rating of 5.0 based on 8 reviews. Book now Why did we suggest this? Your profile celebrates the art of Japanese cuisine, and we noticed your fondness for unique, high-quality ingredients—just like the sea urchin featured in tonight's event. The unexpected ticket availability and tonight’s stellar weather create the perfect, rare opportunity to indulge your senses in a way that aligns with your exquisite taste and love for spontaneous adventure. GET A TICKET HOW TO GET THERE
How can we build amazing personalized contexts?
4 Levels of context personalization 18 LEVEL 1 LEVEL 0 LEVEL 3 LEVEL 2
19 ● None Broad, one-size-fits-all recommendations (TRAD RAG) CONTEXT LEVEL 1 LEVEL 0 LEVEL 3 LEVEL 2 LEVEL 0: No Context
Generate a travel recommendation LEVEL 0: No Context 20 Recommendation LLM Bad Recommendation Uh, Paris? VectorDB Candidates w/ no context
No context High context Quality of response 21 Level 0
CONTEXT LEVEL 1: Batch Context 22 ● Batch LEVEL 1 CONTEXT ● None LEVEL 0 Personalized insights drawn from past behavior and profile data Broad, one-size-fits-all recommendations (The dumbest model) LEVEL 3 LEVEL 2
23 Recommendation LLM Candidates w/ Batch Context Data Warehouse ● Trips history ● User interests ● Favorite activities LEVEL 1: Batch Context Feature Platform Candidates VectorDB
24 Recommendation LLM Candidates w/ Batch Context Data Warehouse ● Trips history ● User interests ● Favorite activities LEVEL 1: Batch Context Feature Platform Candidates Candidate Source 1. Building pipelines to retrieve, serve, and join data from warehouses / data lakes 2. Creating historical eval data sets for benchmarking and development Problems you will encounter
25 Building batch context simply “What are the last 5 places this person has visited?” 1) Write simple definition trip_history_features.py 2) Create Eval Data 4) Read in real-time 3) Deploy to production $ tecton apply
26 ● trips_history ● user_interests ● favorite_activities “Visit the ancient city of Kyoto. Given your interest in history and your extensive travel to historical sites, you'll appreciate the city’s rich heritage and numerous temples.” LEVEL 1: Batch Context Data Warehouse
27 No context High context Quality of response Level 0 Level 1
● Batch ● Streaming LEVEL 2 CONTEXT ● Batch LEVEL 1 CONTEXT LEVEL 2: Batch + Streaming Context 28 ● None LEVEL 0 Personalized insights drawn from past behavior and profile data Recommendations adapted to the user's current interests and interaction behavior Broad, one-size-fits-all recommendations CONTEXT
Recommendation LLM Candidates w/ Batch + Streaming Context VectorDB Candidates 29 LEVEL 2: Batch + Streaming Context Data Warehouse Personalized Recommendation ● Products viewed or interacted with recently ● Purchase trends ● Pricing changes Purchase data Search data Session interactions Feature Platform
Recommendation LLM Candidates w/ Batch + Streaming Context Candidate Source Candidates 30 LEVEL 2: Batch + Streaming Context Data Warehouse Personalized Recommendation ● Products viewed or interacted with recently ● Purchase trends ● Pricing changes Purchase data Search data Session interactions Feature Platform 1. Building, evaluating, productionizing, and monitoring streaming data pipelines 2. Cost-efficient inference (not just the model!) Problems you will encounter 🤯
Building streaming context can also be simple “In the past hour, what topics did the user watch a video about?” 2) Create Eval Data 4) Read in real-time 3) Deploy to production $ tecton apply 31 1) Simple definition media_interaction_features.py
2) Create Eval Data 3) Deploy to production $ tecton apply media_interaction_features.py 4) Read in real-time 1) Simple definition 32 Building streaming context can also be simple “In the past hour, what topics did the user watch a video about?” Same workflow for any context
33 LEVEL 2: Batch + Streaming Context ● locations_viewed_recently ● recent_activities_viewed ● pricing_changes “Considering you've recently been looking at trips to Japan and your recent interest in fine dining, Kyoto's Gion district presents a unique dining adventure with its renowned kaiseki experience. Seasonal ingredients are masterfully crafted into exquisite dishes, offering a feast for the senses. Don’t miss this chance to indulge in Japan's artful cuisine during your stay!" Streaming
No context High context Quality of response Level 0 Level 1 34 Level 2
● Batch ● Streaming ● Real-time LEVEL 3 CONTEXT ● Batch ● Streaming LEVEL 2 CONTEXT ● Batch LEVEL 1 CONTEXT LEVEL 3: Batch + Streaming + Real-time Context 35 ● None LEVEL 0 Personalized insights drawn from past behavior and profile data Recommendations adapted to the user's current interests and interactive behavior Informed, personalized recommendations using live external events, the user’s current context, and real-time inputs Broad, one-size-fits-all recommendations CONTEXT
● Query ● User location ● Local events User Application Data provider ● Local Weather ● Traffic + flight info ● Social media trends Candidates w/ Batch + Streaming + Real-time Context Data Warehouse Recommendation LLM 36 LEVEL 3: Full RAG Batch + Streaming + Real-time Context VectorDB Candidates Purchase data Purchase data Session interactions Feature Platform Personalized Recommendation
● Query ● User location ● Local events User Application Data provider ● Local Weather ● Traffic + flight info ● Social media trends Candidates w/ Batch + Streaming + Real-time Context Data Warehouse Recommendation LLM 37 LEVEL 3: Batch + Streaming + Real-time Context Candidate Source Candidates Purchase data Purchase data Session interactions Feature Platform Personalized Recommendation 1. Building, evaluating, productionizing, and monitoring real-time data pipelines 2. Integrating 3rd party real-time data sources 3. Striking the right balance between speed and cost Problems you will encounter ��
38 Building real-time context works the same way “How far is the user from the destination? Same country?” 1) Write simple definition device_destination_distance_features.py …the other steps are the same
39 Building real-time context works the same way “What’s the weather like in that place right now?” …the other steps are the same destination_weather_features.py 1) Write simple definition
Last-Minute Opening: A few coveted spots at Chef Takumi Nishimura's 'Sushi Mastery' workshop have just opened up—right in the heart of Gion, a few minutes walk from you. Seize this rare chance to handcraft the praised dragonfly roll, adorned with top-choice sea urchin, as you've recently blogged about. The forecast promises a perfect evening with clear skies to enjoy this gastronomic affair. The workshop has Dassai Umeshu 23 sake that you've been eager to try. Act now; these tickets won’t last! Real-time LEVEL 3: Batch + Streaming + Real-time Context 40 ● query ● user_location ● local_events ● local_weather ● traffic_and_flights ● social_media_trends
Real-time personalization means more trusted and valuable recommendations Tonight: Sushi at Festival in Gion! 41 Last-Minute Opening: A few coveted spots at Chef Takumi Nishimura's 'Sushi Mastery' workshop have just opened up—right in the heart of Gion, a few minutes walk from you. Seize this rare chance to handcraft the praised dragonfly roll, adorned with top-choice sea urchin, as you've recently blogged about. The forecast promises a perfect evening with clear skies to enjoy this gastronomic affair. The workshop has Dassai Umeshu 23 sake that you've been eager to try. Act now; these tickets won’t last! Where to stay First, we have the Hotel Mume located at 東山区新門前通梅本町 261. This amazing hotel has an outstanding average rating of 5.0 based on 8 reviews. Book now Following closely is Shiraume at 東山区祗園新橋白川筋. Also boasting an average rating of 5.0 from 12 reviews, it's highly recommended and beloved by previous travelers. Book now Lastly, we have the SUIRAN LUXURY COLLECTION HOTEL KYOTO located at 右京区嵯峨天 龍寺芒ノ馬場町12. This luxurious hotel in Kyoto also got an average rating of 5.0 based on 8 reviews. Book now Why did we suggest this? Your profile celebrates the art of Japanese cuisine, and we noticed your fondness for unique, high-quality ingredients—just like the sea urchin featured in tonight's event. The unexpected ticket availability and tonight’s stellar weather create the perfect, rare opportunity to indulge your senses in a way that aligns with your exquisite taste and love for spontaneous adventure. GET A TICKET HOW TO GET THERE
42 ● Batch ● Streaming ● Real-time LEVEL 3 CONTEXT ● Batch ● Streaming LEVEL 2 CONTEXT ● Batch LEVEL 1 CONTEXT BONUS LEVEL 4 Real-time Context w/ feedback ● None LEVEL 0 Personalized insights drawn from past behavior and profile data Recommendations adapted to the user's current interests and interactive behavior Informed, personalized recommendations using live external events, the user’s current context, and real-time inputs Broad, one-size-fits-all recommendations CONTEXT ● Batch ● Streaming ● Real-time with feedback LEVEL 4 CONTEXT Informed, personalized recommendations using live external events, the user’s current context, and real-time inputs IN CONCEPT
OK, what did we learn?
44 Context is King! E-commerce Tailored shopping experiences Communication Conversational AI that understands you Content Recommendations that resonate Health & Wellness Customized wellbeing plans Financial Services Personal financial advice
45 Personalizing context can unlock amazing AI behaviors and product experiences. 1 Higher degrees of personalization are more valuable but harder to build. 2 Feature Platforms can configure and assemble personalized context for LLMs. 3
User Application Data provider Context Data Warehouse Recommendation model 46 VectorDB Purchase data Purchase data Session interactions Feature Platform
User Application Data provider Context Data Warehouse Recommendation LLM 47 Candidate Source Purchase data Purchase data Session interactions Feature Platform ● Versioning ● Collaboration ● Governance ● Debuggability ● Monitoring and Alerting Other problems you’ll run into on your journey
Build a Full RAG today 48 …and solve all your other AI data problems Get started at tecton.ai/explore
ANNOUNCING Rift is now in Public Preview Python is all you need Python transformations for batch, streaming, & real-time. Unmatched performance Millisecond-fresh aggregations across millions of events. Try Rift now: tecton.ai/explore Lightning-fast iteration Develop & test locally. Productionize instantly. The world’s fastest path to real-time AI.

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Full-RAG: A modern architecture for hyper-personalization

  • 1. Full-RAG: A modern architecture for hyper-personalization Mike Del Balso CEO & Co-Founder
  • 2. 2 Goal: highly personalized travel recommendations We think you’ll love ?
  • 3. How can we get a good suggestion from a model? 3
  • 4. 4 Fine tune? Improving model’s intrinsic knowledge How can we get a better recommendation? Prompt Engineer? Rewording the question, giving time to think RAG? Improving model’s knowledge about the current situation
  • 5. 5 Low quality recommendation content VectorDB Candidates: Cities Recommendation LLM Traditional RAG (Stone age) City: Paris Country: France City: Tokyo Country: France City: Johannesburg Country: South Africa 🙁 “Visit Paris!” Uncontextualized Candidates Query: Where should I travel over summer break?
  • 6. 6 Where should I travel over summer break? Stranger Travel Agent Low context, High expertise Paris. Your Best Friend Travel Agent High context, High Expertise You said you loved that sailing trip last summer, why not go check out the Rodos Cup in Greece? Rhodes has a super cool old town with lots of great little cafés.
  • 7. 7 Without context (i.e. in Trad RAG), we just have uncontextualized candidates Uncontextualized Candidate City: Paris Country: France City: Tokyo Country: Japan
  • 8. Context is the relevant information that AI models use to understand a situation and make decisions. 8
  • 9. 9 Context enriches the candidate with more information to make it easier to reason about Uncontextualized Candidate Contextualized Candidate City: Paris Country: France Weather: 20°C, sunny Activities: Museums, cafes, river tours Nature: Fontainebleau, Versailles gardens Events: Fashion Week, Bastille Day Cuisine: Croissants, escargot Language: French Cost/Day: 200 USD Safety: High Visit Time: Apr-Jun, Sep-Nov Accessibility: High, extensive public transport Historic Sites: Eiffel Tower, Notre Dame Accommodation Range: Hostels to luxury hotels Visa Ease: Schengen Area, visa policies vary Nightlife: Vibrant, diverse options Family Friendly: Yes, many activities Art Scene: Louvre, Montmartre Shopping: Boutiques, flea markets Internet Access: High-speed, widely available City: Paris Country: France
  • 10. City: Paris Country: France City: Paris Country: France Weather: 20°C, sunny Activities: Museums, cafes, river tours Nature: Fontainebleau, Versailles gardens Events: Fashion Week, Bastille Day Cuisine: Croissants, escargot Language: French Cost/Day: 200 USD Safety: High Visit Time: Apr-Jun, Sep-Nov Accessibility: High, extensive public transport Historic Sites: Eiffel Tower, Notre Dame Accommodation Range: Hostels to luxury hotels Visa Ease: Schengen Area, visa policies vary Nightlife: Vibrant, diverse options Family Friendly: Yes, many activities Art Scene: Louvre, Montmartre Shopping: Boutiques, flea markets Internet Access: High-speed, widely available 10 Personalized Context enriches candidates with user-level information Without context With context With Personalized Context City: Paris Country: France Weather: 20°C, sunny Activities: Museums, cafes, river tours Nature: Fontainebleau, Versailles gardens … Preferred Climate: Mild Interest in History: High Dining Preference: Gourmet/Fine dining Cultural Interest: High in arts and fashion Budget: Luxury Accommodation Preference: Boutique hotels Preferred Language: Prefers English-friendly destinations Activity Level: Moderate, enjoys leisurely strolls and seated activities Travel Experience: Seasoned traveler, prefers depth of experience Travel Group: Solo traveler Interest in Shopping: High, prefers unique boutiques Nightlife Interest: Low, prefers quiet evenings Interest in Local Cuisine: High, enjoys trying national dishes Interest in Events: Moderate, selectively attends major events Transportation Preference: Public transport, occasional taxi
  • 11. 11 Examples of context Destination Insights Cultural Significance Local customs, events, and holidays at the destination Safety and Alerts Current travel advisories and safety warnings Attractions and Activities Information on points of interest and things to do Lodging and Transport Availability and options for accommodation and local travel User-Centric Data Historical Interactions Including search history and previous bookings Demographic Information Age, language preferences, and other personal data Travel Patterns Data on past destinations and types of travel Preferences and Real-Time Data Activity Monitoring User's current engagement with the platform Active Input Immediate queries and filter settings Preference Settings Explicitly stated travel preferences and interests Situational Context Geographic Position The user’s current or selected location Temporal Context Time of day, date, and season Economic Context Financial Indicators User's budget range and previous spending habits Currency Trends Current exchange rates affecting travel costs External Influences Event Schedules Local events that could impact or enhance the travel experience Weather Patterns Forecasted weather conditions for the destination ... ... ...
  • 13. 13 VectorDB Candidates Recommendation LLM Create personalized context by enriching candidates with relevant user data City: Paris Country: France City: Tokyo Country: France City: Johannesburg Country: South Africa Feature Platform Candidate Data User Data Candidates w/ personalized context Best Friend-level Travel Agent recommendation “Kyoto is perfect for you because…” The Feature platform orchestrates context assembly
  • 14. Frequency of city vs. countryside destinations Likelihood to engage in water sports Historical landmark visitation history Language proficiency for non-English destinations Desire for luxury vs. budget accommodations Appreciation for local music and performance arts Engagement with nature and wildlife conservation areas Interest in volunteer tourism opportunities Local public transportation efficiency Accessibility of medical facilities in the destination Economic stability of the destination country Political climate's impact on tourist safety Visa and entry requirements for the destination Current exchange rate advantages Local health advisories or travel restrictions Event timing, such as major sports or cultural events Availability of direct vs. connecting flights Seasonal tourist crowd levels Local peak dining times and availability Regional security advisories Cultural norms and attire expectations Time zone differences affecting activity planning Environmental sustainability initiatives at the destination Local telecommunications infrastructure for connectivity Historical weather patterns for planned travel dates Area-specific traveler reviews and ratings Local emergency services and language support Average local costs for tourists Destination-specific travel insurance recommendations Local customs clearance processes for travelers Recent developments in local tourism facilities Availability of multilingual guided tours Best High personalization → Better recommendations VectorDB Candidates Recommendation LLM City: Paris Country: France City: Tokyo Country: France City: Johannesburg Country: South Africa Destination Data Candidates w/ Context Feature Platform 14 User Data
  • 15. We think you’ll love 15 Where to stay ?
  • 16. Tonight: Sushi at Festival in Gion! 16 Last-Minute Opening: A few coveted spots at Chef Takumi Nishimura's 'Sushi Mastery' workshop have just opened up—right in the heart of Gion, a few minutes walk from you. Seize this rare chance to handcraft the praised dragonfly roll, adorned with top-choice sea urchin, as you've keenly blogged about. The forecast promises a perfect evening with clear skies to enjoy this gastronomic affair. The workshop has Dassai Umeshu 23 sake that you've been eager to try. Act now; these tickets won’t last! Where to stay First, we have the Hotel Mume located at 東山区新門前通梅本 町261. This amazing hotel has an outstanding average rating of 5.0 based on 8 reviews. Book now Following closely is Shiraume at 東山区祗園新橋白川筋 . Also boasting an average rating of 5.0 from 12 reviews, it's highly recommended and beloved by previous travelers. Book now Lastly, we have the SUIRAN LUXURY COLLECTION HOTEL KYOTO located at 右京区嵯峨 天 龍寺芒ノ馬場町 12. This luxurious hotel in Kyoto also got an average rating of 5.0 based on 8 reviews. Book now Why did we suggest this? Your profile celebrates the art of Japanese cuisine, and we noticed your fondness for unique, high-quality ingredients—just like the sea urchin featured in tonight's event. The unexpected ticket availability and tonight’s stellar weather create the perfect, rare opportunity to indulge your senses in a way that aligns with your exquisite taste and love for spontaneous adventure. GET A TICKET HOW TO GET THERE
  • 17. How can we build amazing personalized contexts?
  • 18. 4 Levels of context personalization 18 LEVEL 1 LEVEL 0 LEVEL 3 LEVEL 2
  • 19. 19 ● None Broad, one-size-fits-all recommendations (TRAD RAG) CONTEXT LEVEL 1 LEVEL 0 LEVEL 3 LEVEL 2 LEVEL 0: No Context
  • 20. Generate a travel recommendation LEVEL 0: No Context 20 Recommendation LLM Bad Recommendation Uh, Paris? VectorDB Candidates w/ no context
  • 21. No context High context Quality of response 21 Level 0
  • 22. CONTEXT LEVEL 1: Batch Context 22 ● Batch LEVEL 1 CONTEXT ● None LEVEL 0 Personalized insights drawn from past behavior and profile data Broad, one-size-fits-all recommendations (The dumbest model) LEVEL 3 LEVEL 2
  • 23. 23 Recommendation LLM Candidates w/ Batch Context Data Warehouse ● Trips history ● User interests ● Favorite activities LEVEL 1: Batch Context Feature Platform Candidates VectorDB
  • 24. 24 Recommendation LLM Candidates w/ Batch Context Data Warehouse ● Trips history ● User interests ● Favorite activities LEVEL 1: Batch Context Feature Platform Candidates Candidate Source 1. Building pipelines to retrieve, serve, and join data from warehouses / data lakes 2. Creating historical eval data sets for benchmarking and development Problems you will encounter
  • 25. 25 Building batch context simply “What are the last 5 places this person has visited?” 1) Write simple definition trip_history_features.py 2) Create Eval Data 4) Read in real-time 3) Deploy to production $ tecton apply
  • 26. 26 ● trips_history ● user_interests ● favorite_activities “Visit the ancient city of Kyoto. Given your interest in history and your extensive travel to historical sites, you'll appreciate the city’s rich heritage and numerous temples.” LEVEL 1: Batch Context Data Warehouse
  • 27. 27 No context High context Quality of response Level 0 Level 1
  • 28. ● Batch ● Streaming LEVEL 2 CONTEXT ● Batch LEVEL 1 CONTEXT LEVEL 2: Batch + Streaming Context 28 ● None LEVEL 0 Personalized insights drawn from past behavior and profile data Recommendations adapted to the user's current interests and interaction behavior Broad, one-size-fits-all recommendations CONTEXT
  • 29. Recommendation LLM Candidates w/ Batch + Streaming Context VectorDB Candidates 29 LEVEL 2: Batch + Streaming Context Data Warehouse Personalized Recommendation ● Products viewed or interacted with recently ● Purchase trends ● Pricing changes Purchase data Search data Session interactions Feature Platform
  • 30. Recommendation LLM Candidates w/ Batch + Streaming Context Candidate Source Candidates 30 LEVEL 2: Batch + Streaming Context Data Warehouse Personalized Recommendation ● Products viewed or interacted with recently ● Purchase trends ● Pricing changes Purchase data Search data Session interactions Feature Platform 1. Building, evaluating, productionizing, and monitoring streaming data pipelines 2. Cost-efficient inference (not just the model!) Problems you will encounter 🤯
  • 31. Building streaming context can also be simple “In the past hour, what topics did the user watch a video about?” 2) Create Eval Data 4) Read in real-time 3) Deploy to production $ tecton apply 31 1) Simple definition media_interaction_features.py
  • 32. 2) Create Eval Data 3) Deploy to production $ tecton apply media_interaction_features.py 4) Read in real-time 1) Simple definition 32 Building streaming context can also be simple “In the past hour, what topics did the user watch a video about?” Same workflow for any context
  • 33. 33 LEVEL 2: Batch + Streaming Context ● locations_viewed_recently ● recent_activities_viewed ● pricing_changes “Considering you've recently been looking at trips to Japan and your recent interest in fine dining, Kyoto's Gion district presents a unique dining adventure with its renowned kaiseki experience. Seasonal ingredients are masterfully crafted into exquisite dishes, offering a feast for the senses. Don’t miss this chance to indulge in Japan's artful cuisine during your stay!" Streaming
  • 34. No context High context Quality of response Level 0 Level 1 34 Level 2
  • 35. ● Batch ● Streaming ● Real-time LEVEL 3 CONTEXT ● Batch ● Streaming LEVEL 2 CONTEXT ● Batch LEVEL 1 CONTEXT LEVEL 3: Batch + Streaming + Real-time Context 35 ● None LEVEL 0 Personalized insights drawn from past behavior and profile data Recommendations adapted to the user's current interests and interactive behavior Informed, personalized recommendations using live external events, the user’s current context, and real-time inputs Broad, one-size-fits-all recommendations CONTEXT
  • 36. ● Query ● User location ● Local events User Application Data provider ● Local Weather ● Traffic + flight info ● Social media trends Candidates w/ Batch + Streaming + Real-time Context Data Warehouse Recommendation LLM 36 LEVEL 3: Full RAG Batch + Streaming + Real-time Context VectorDB Candidates Purchase data Purchase data Session interactions Feature Platform Personalized Recommendation
  • 37. ● Query ● User location ● Local events User Application Data provider ● Local Weather ● Traffic + flight info ● Social media trends Candidates w/ Batch + Streaming + Real-time Context Data Warehouse Recommendation LLM 37 LEVEL 3: Batch + Streaming + Real-time Context Candidate Source Candidates Purchase data Purchase data Session interactions Feature Platform Personalized Recommendation 1. Building, evaluating, productionizing, and monitoring real-time data pipelines 2. Integrating 3rd party real-time data sources 3. Striking the right balance between speed and cost Problems you will encounter ��
  • 38. 38 Building real-time context works the same way “How far is the user from the destination? Same country?” 1) Write simple definition device_destination_distance_features.py …the other steps are the same
  • 39. 39 Building real-time context works the same way “What’s the weather like in that place right now?” …the other steps are the same destination_weather_features.py 1) Write simple definition
  • 40. Last-Minute Opening: A few coveted spots at Chef Takumi Nishimura's 'Sushi Mastery' workshop have just opened up—right in the heart of Gion, a few minutes walk from you. Seize this rare chance to handcraft the praised dragonfly roll, adorned with top-choice sea urchin, as you've recently blogged about. The forecast promises a perfect evening with clear skies to enjoy this gastronomic affair. The workshop has Dassai Umeshu 23 sake that you've been eager to try. Act now; these tickets won’t last! Real-time LEVEL 3: Batch + Streaming + Real-time Context 40 ● query ● user_location ● local_events ● local_weather ● traffic_and_flights ● social_media_trends
  • 41. Real-time personalization means more trusted and valuable recommendations Tonight: Sushi at Festival in Gion! 41 Last-Minute Opening: A few coveted spots at Chef Takumi Nishimura's 'Sushi Mastery' workshop have just opened up—right in the heart of Gion, a few minutes walk from you. Seize this rare chance to handcraft the praised dragonfly roll, adorned with top-choice sea urchin, as you've recently blogged about. The forecast promises a perfect evening with clear skies to enjoy this gastronomic affair. The workshop has Dassai Umeshu 23 sake that you've been eager to try. Act now; these tickets won’t last! Where to stay First, we have the Hotel Mume located at 東山区新門前通梅本町 261. This amazing hotel has an outstanding average rating of 5.0 based on 8 reviews. Book now Following closely is Shiraume at 東山区祗園新橋白川筋. Also boasting an average rating of 5.0 from 12 reviews, it's highly recommended and beloved by previous travelers. Book now Lastly, we have the SUIRAN LUXURY COLLECTION HOTEL KYOTO located at 右京区嵯峨天 龍寺芒ノ馬場町12. This luxurious hotel in Kyoto also got an average rating of 5.0 based on 8 reviews. Book now Why did we suggest this? Your profile celebrates the art of Japanese cuisine, and we noticed your fondness for unique, high-quality ingredients—just like the sea urchin featured in tonight's event. The unexpected ticket availability and tonight’s stellar weather create the perfect, rare opportunity to indulge your senses in a way that aligns with your exquisite taste and love for spontaneous adventure. GET A TICKET HOW TO GET THERE
  • 42. 42 ● Batch ● Streaming ● Real-time LEVEL 3 CONTEXT ● Batch ● Streaming LEVEL 2 CONTEXT ● Batch LEVEL 1 CONTEXT BONUS LEVEL 4 Real-time Context w/ feedback ● None LEVEL 0 Personalized insights drawn from past behavior and profile data Recommendations adapted to the user's current interests and interactive behavior Informed, personalized recommendations using live external events, the user’s current context, and real-time inputs Broad, one-size-fits-all recommendations CONTEXT ● Batch ● Streaming ● Real-time with feedback LEVEL 4 CONTEXT Informed, personalized recommendations using live external events, the user’s current context, and real-time inputs IN CONCEPT
  • 43. OK, what did we learn?
  • 44. 44 Context is King! E-commerce Tailored shopping experiences Communication Conversational AI that understands you Content Recommendations that resonate Health & Wellness Customized wellbeing plans Financial Services Personal financial advice
  • 45. 45 Personalizing context can unlock amazing AI behaviors and product experiences. 1 Higher degrees of personalization are more valuable but harder to build. 2 Feature Platforms can configure and assemble personalized context for LLMs. 3
  • 46. User Application Data provider Context Data Warehouse Recommendation model 46 VectorDB Purchase data Purchase data Session interactions Feature Platform
  • 47. User Application Data provider Context Data Warehouse Recommendation LLM 47 Candidate Source Purchase data Purchase data Session interactions Feature Platform ● Versioning ● Collaboration ● Governance ● Debuggability ● Monitoring and Alerting Other problems you’ll run into on your journey
  • 48. Build a Full RAG today 48 …and solve all your other AI data problems Get started at tecton.ai/explore
  • 49. ANNOUNCING Rift is now in Public Preview Python is all you need Python transformations for batch, streaming, & real-time. Unmatched performance Millisecond-fresh aggregations across millions of events. Try Rift now: tecton.ai/explore Lightning-fast iteration Develop & test locally. Productionize instantly. The world’s fastest path to real-time AI.
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