AI Memory vs RAG vs Fine-Tuning

Your AI coding assistant forgets everything between sessions. You want to fix that. Three approaches exist, and they solve different problems.

Fine-Tuning

What it is: Modify the model's weights by training on your data.

When it fails for coding assistants: - You can't fine-tune frontier models on your specific codebase - Fine-tuning is static — your codebase changes daily - It doesn't give you retrievable facts - You can't inspect what the model "learned" - Not portable across models

Verdict: Wrong tool for codebase-specific, rapidly-changing knowledge.

RAG (Retrieval-Augmented Generation)

What it is: Chunk your documents, embed them as vectors, retrieve relevant chunks at query time.

When it fails for coding assistants: - Chunking destroys structure. Code has hierarchical meaning that 512-token chunks lose. - Retrieval is reactive. RAG finds documents when you ask. Coding assistants need proactive context. - No knowledge accumulation. RAG doesn't learn from the AI's own work. - No semantic relationships. Similar chunks ≠ connected concepts.

Verdict: Good for document Q&A. Insufficient for persistent coding context.

Persistent Memory (the Memento approach)

What it is: A dedicated memory service the AI reads from at session start and writes to during work.

Verdict: Built for the coding assistant use case.

Which Should You Use?

• "I want my AI to know my codebase across sessions" → Persistent memory
• "I want my chatbot to answer questions about docs" → RAG
• "I want the model to always use a specific format" → Fine-tuning
• "I want my team's AI to share conventions" → Persistent memory
• "I want AI that compounds knowledge over months" → Persistent memory

Try Memento — persistent AI memory for coding assistants →