RAG vs Memory

Quick answer

RAG (Retrieval-Augmented Generation) pulls relevant chunks from a document corpus: PDFs, wikis, product docs, support articles. The knowledge is shared across all users.

AI memory stores facts about individual users: preferences, history, decisions, and context from past conversations. The knowledge is scoped per userId.

Use RAG when the answer lives in company documents. Use memory when the answer depends on who is asking.

Side-by-side comparison

• Data source: RAG uses static documents; memory uses dynamic user-specific facts
• Scope: RAG is typically global or tenant-wide; memory is per user or per session namespace
• Update frequency: RAG re-indexes when docs change; memory updates on every store call
• Retrieval: RAG finds doc chunks; memory finds semantic matches to user context
• Build complexity: RAG needs chunking, embeddings, and pipeline tuning; memory APIs handle this for you

What RAG does not solve

RAG cannot tell the model that this specific user prefers dark mode, uses PostgreSQL, or talked about a billing issue last week unless that information is written into a document and retrieved by chance.

User-specific context changes constantly. Re-indexing chat logs as documents is fragile, expensive, and privacy-sensitive.

That gap is exactly what a memory layer fills.

What memory does not replace

Memory is not a substitute for a product knowledge base. If users ask how to reset a password, the answer should come from your docs via RAG, not from stored user memories.

Memory records are short factual statements, not full document archives. Keep docs in RAG and user facts in memory.

Recommended architecture: RAG + memory together

• Step 1: Recall user memories for the current userId and query
• Step 2: RAG search your doc index for product or policy answers
• Step 3: Merge both into the system prompt with clear sections
• Step 4: Call the LLM with enriched context
• Step 5: Store new durable facts from the conversation with memory.store

const [memories, docs] = await Promise.all([
  memory.recall({ userId, query: userMessage, limit: 5 }),
  ragSearch(userMessage, { topK: 3 }),
]);

const systemPrompt = `
User context:
${memories.map(m => m.text).join("\n")}

Product docs:
${docs.map(d => d.content).join("\n\n")}
`;

Where Databaset fits

Databaset is the memory layer, not the RAG layer. Pair it with Pinecone, pgvector, or any doc search for a complete stack.

One API for store, recall, and forget. Embeddings and semantic ranking included. Works alongside your existing RAG pipeline.