Rag And Memory

---
name: rag-and-memory
description: Patterns for Retrieval-Augmented Generation (RAG) and agent memory systems. Retrieves only relevant context, prevents context bloat, and maintains coherent state across sessions.
category: build
applies-to: [claude, gemini, cursor, copilot, any]
version: 1.0.0
---

## Overview

RAG and memory systems are how AI agents work with knowledge that exceeds their context window. Done well: agents give accurate, grounded answers. Done poorly: context overflow, hallucination from stale retrieval, and performance degradation.

This skill covers the design principles and failure modes of RAG and memory architectures for production AI systems.

## When to Use

- Building any AI system that needs to access external knowledge
- When agent context windows are being exceeded
- When agents need to remember information across sessions
- When building Q&A, document analysis, or knowledge base systems

## Process

### Step 1: Choose the Right Memory Architecture

1. Identify what the agent needs to remember:
   - **Ephemeral**: Within a single session (use in-context memory)
   - **Session-persistent**: Across a user's sessions (use external key-value store)
   - **Knowledge base**: Organizational or domain knowledge (use vector DB + RAG)
   - **Procedural**: How to do tasks (encode in SKILL.md / system prompt)
2. Match the memory type to the store:

| Memory Type | Recommended Store |
|------------|------------------|
| In-session facts | Context window (summarized) |
| User preferences | Key-value store (Redis, DynamoDB) |
| Document corpus | Vector database (Pinecone, Weaviate, pgvector) |
| Long-term facts | Structured DB + caching |

**Verify:** Each type of information the agent needs has a defined storage mechanism.

### Step 2: Design the RAG Pipeline

3. **Chunking strategy**: Break documents into chunks at semantic boundaries (paragraphs, sections) — not arbitrary character counts.
4. **Embedding model**: Match the embedding model to your query type. Use the same model for indexing and retrieval.
5. **Retrieval**: Retrieve top-K most semantically similar chunks. K = 3–7 is usually optimal.
6. **Re-ranking**: After retrieval, re-rank by relevance using a cross-encoder. Top K becomes top 3–5 for the prompt.
7. **Context injection**: Inject retrieved chunks into the prompt with clear source citations.

**Verify:** Retrieved chunks are genuinely relevant to the query before injecting into context.

### Step 3: Prevent Context Bloat

8. **Summarize, don't accumulate**: For long sessions, summarize previous turns rather than appending them indefinitely.
9. **Retrieve, don't pre-load**: Only load context relevant to the current query. Don't pre-load everything.
10. **Set context budgets**: Define maximum token allocations for: system prompt, retrieved context, conversation history, user message.
11. **Compress before injecting**: Summarize long retrieved documents to extract the relevant portion only.

**Verify:** Total prompt length is within model limits with buffer. Retrieved context is relevant to current query.

### Step 4: Handle Retrieval Failures Gracefully

12. If retrieval returns no relevant results: say so — do not hallucinate an answer.
13. If retrieved documents are outdated: surface the document date to the user.
14. If confidence is low: present the retrieved source and let the user evaluate.
15. Design for "no relevant information found" as a first-class outcome.

**Verify:** System has defined behavior for failed/empty retrieval.

### Step 5: Measure and Optimize

16. Track retrieval quality:
    - **Precision**: Are retrieved chunks relevant to the query?
    - **Recall**: Are relevant chunks being retrieved at all?
17. Track answer quality: Use RAGAS or similar evaluation framework.
18. Monitor: context length per query, retrieval latency, hallucination rate.

**Verify:** Baseline metrics established. Retrieval precision > 80%.

## Common Rationalizations (and Rebuttals)

| Excuse | Rebuttal |
|--------|----------|
| "Let's just put everything in the context" | Context bloat degrades quality and costs money. Retrieve what's needed. |
| "The model knows this from training" | Training knowledge is stale. Use RAG for current information. |
| "Vector search is good enough without re-ranking" | Re-ranking improves precision significantly. It's a small cost for large quality gain. |
| "We'll fix retrieval quality later" | Poor retrieval quality compounds into poor answer quality. Fix it now. |

## Red Flags

- Entire document corpus pre-loaded into every prompt
- Retrieval returning chunks from unrelated documents
- No defined behavior for empty retrieval results
- Context window regularly at 90%+ capacity
- Agent answering from "training knowledge" instead of retrieved documents
- No source citations for retrieved information

## Verification

- [ ] Memory architecture matches the type of information needed
- [ ] RAG pipeline: chunk → embed → retrieve → re-rank → inject
- [ ] Context budgets defined for all prompt sections
- [ ] Empty retrieval has a defined graceful fallback
- [ ] Retrieval precision measured and > 80%
- [ ] Source citations included in AI responses

## References

- [hallucination-prevention skill](../hallucination-prevention/SKILL.md)
- [multi-agent-orchestration skill](../multi-agent-orchestration/SKILL.md)
- [ai-output-validation skill](../ai-output-validation/SKILL.md)