Welcome to 6.S093!

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Memory and RAG

January 23rd, 2026

Eduardo Baena / Konstantina Yaneva

MIT 6.S093 How to ship almost anything with AI

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About Me

• Eduardo Baena
• Researcher in AI for Space Wireless Infrastructure
• Sundai Hacker
• Philabs Co Founder

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The Dream

Imagine your ideal SM AI Agent that…

• Writes amazing SM posts
• Replies to comments
• Remembers what work and what it didn’t
• Learns and improves over time

Image created with Nano Banana

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Objective: Reality

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Why can’t we just retrain?

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Why can’t we just retrain?

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Context Engineering (CE)

CE is the deliberate design of everything that enters the LLM's context window to achieve desired behavior

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RAG: A Context Engineering Technique

Retrieval-Augmented Generation (RAG) is one specific technique within CE

The analogy

• Without RAG: Student takes exam from memory only
• With RAG: Student can consult notes

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RAG: A Context Engineering Technique

Retrieval-Augmented Generation (RAG) is one specific technique within CE

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What is an Embedding?

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What is an Embedding?

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Embedding Space

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Embedding Space

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RAG: Simple breakdown

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What is a Vector Database?

A vector database stores embeddings and enables similarity search

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What is a Vector Database?

A vector database stores embeddings and enables similarity search

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Indexing: Why It Matters

Problem: Comparing query to ALL vectors is slow: O(n)

Solution: Indexing organizes vectors for faster search: O(log n)

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Indexing: Why It Matters

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Similarity Metrics: How to Compare?

Three main ways to measure (semantic) similarity

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Similarity Metrics: How to Compare?

Three main ways to measure (semantic) similarity

Start with cosine similarity it's the most common and works well for text.

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Metadata Filtering

Metadata allows filtering results by attributes:

Example query: Find ML posts with ≥50 likes from last month

• Filter by metadata: likes >= 50 AND date > '2024-12-01'
• Then rank by semantic similarity to ML

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Hands On: Vector Database Options

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The Complete RAG Pipeline

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Step 1: Setup Database and Store Documents

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Step 2: Index Documents

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Step 3: Search by Similarity

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Step 4: Build Prompt with Retrieved Context

This is the Augment step: injecting retrieved docs into the prompt

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Step 5: Generate with LLM

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Welcome to RAG World

BASIC RAG CONCEPTS THAT YOU SHOULD KNOW

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Chunking

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Chunking

Bad chunking example:

• Document: The meeting is on Tuesday at 3pm in Room 101.
• Chunk 1: The meeting is on Tuesday
• Chunk 2: at 3pm in Room 101 

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Chunking Strategies

https://gustavo-espindola.medium.com/chunk-division-and-overlap-understanding-the-process-ade7eae1b2bd

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Chunking for Social Media Posts

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Precision and Recall: Trade-off

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Precision and Recall: When to Prioritize

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Keywords VS Embeddings

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Hybrid Search: Best of Both Worlds

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Hybrid Search: Best of Both Worlds

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RAG vs Memory: What's the Difference?

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Memory Extension 1: Outcome-Aware Retrieval

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Memory Extension 2: Human-in-the-Loop (HITL)

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Memory Extension 3: Temporal Evolution

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RAG Challenges

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SoA: Infinite Context?

RLM @ MIT

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Key Takeaways

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Key Takeaways

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RAG & Memory

Questions?

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Now: lunch break!

Sundai Club Team

January, 2025

Mini test for today's lecture.

Please fill in to get the attendance!��https://forms.gle/PAYNjtQUnm1Pbzxz8

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