In partnership with

Why memory matters

LLMs are stateless by design. They read a prompt, generate tokens, then stop. They do not remember past calls. That’s fine for single questions. It’s a problem for long conversations, personalization, or time-sensitive facts.

So you add memory outside the model. That choice affects cost, latency, complexity, and trust. Pick the simplest option that meets your needs. Don’t overbuild.

Two kinds of knowledge

There are two broad buckets.

• Intrinsic knowledge. That’s what the model learned during training. It is fixed inside the model weights. It covers general facts up to the training cutoff.

• External memory. That’s data you add during inference. It can be recent, private, or domain specific. It lives outside the model and can be updated.

You combine both when you need accuracy and personalization.

Turn AI Into Extra Income

You don’t need to be a coder to make AI work for you. Subscribe to Mindstream and get 200+ proven ideas showing how real people are using ChatGPT, Midjourney, and other tools to earn on the side.

From small wins to full-on ventures, this guide helps you turn AI skills into real results, without the overwhelm.

Get Your Free Guide

A simple taxonomy of memory

Think of memory like human memory mapped to system parts.

• Working memory (context window). Short term. Fast but limited. Holds the current prompt, recent chat, and outputs.

• Episodic memory. Facts about a user or past important events. Persistent across sessions.

• Long-term external memory (RAG). Large, searchable knowledge stores. Good for documents and up-to-date facts.

Each layer has a role. Use them together when needed.

Context window: the model’s RAM

The context window is the model’s built-in working memory. It includes everything the model can look at in one inference: prompt, history, and injected context.

Key facts:

• Larger windows let the model consider more text at once.

• Bigger windows usually improve coherence. They can lower hallucinations for long tasks.

• But attention costs grow fast. Processing many tokens is expensive and slower.

• Even very large windows still need relevance. Quantity does not equal quality.

Practical tips:

• Keep the context focused. Feed only what's necessary.

• Use sliding windows or trimming to keep token counts reasonable.

• Use summarization to compress old history, but know it loses detail.

Retrieval-Augmented Generation (RAG): external long term memory

RAG adds a retrieval step before generation. It finds relevant documents and feeds them into the model. This fixes gaps in the model’s training data and reduces hallucination.

How it works, simply:

1. Index documents and split them into chunks.

2. Convert chunks to vectors using an embedding model.

3. Store vectors in a vector database.

4. At query time, vectorize the user query.

5. Retrieve the top matches and inject them into the prompt.

Strengths:

• Keeps facts current without retraining the model.

• Supports citations and verifiable answers.

• Often cheaper than feeding huge contexts into the model.

Limitations:

• Retrieval is only as good as indexing and chunking.

• Bad queries or weak embeddings can miss relevant docs.

• Conflicting sources can confuse the model.

• The pipeline adds points of failure.

Practical tips:

• Design a consistent chunking strategy. Don’t split in the middle of a concept.

• Re-rank retrieved items when possible.

• Update embeddings and indexes on a schedule that matches data change rates.

Advanced memory: selective, structured, and episodic storage

Storing every chat transcript wastes tokens and creates noise. Better approaches extract and store only the important facts.

• Selective retention. Save attributes that matter: preferences, allergies, long-term tasks, or project details.

• Structured memory. Store facts in labeled fields or vector entries. Don’t store raw transcripts unless you need them.

• Episodic retrieval. At query time, search user memory as well as external knowledge. Inject only the most relevant memories.

This approach keeps personalization without bloating the context.

Trade-offs and the synergy model

You have two main architectural paths.

• RAG first. Use retrieval to fetch relevant data. Feed the model a small, high-value context. This is cost-efficient and easier to debug.

• Long-context first. Give the model everything in one big prompt. This works for single, deep reads but costs more and scales poorly.

A hybrid works best for many real apps:

1. Use RAG to fetch focused facts.

2. Feed those facts into a large context window when deeper reasoning is needed.

3. Use selective memory for personalization.

This combination balances cost, accuracy, and reasoning power.

Practical decision checklist

Use this checklist to pick a design:

1. Is the task single turn or multi-session?

   • Single turn with all data in prompt → long-context may work.

   • Multi-session or personalization → need episodic memory.

2. Does the app need up-to-date or proprietary facts?

   • Yes → use RAG.

   • No → intrinsic knowledge might suffice.

3. How sensitive are mistakes?

   • High sensitivity (legal, medical) → RAG with citations and strict validation.

4. What is the cost tolerance?

   • Low tolerance → favor RAG + small context.

   • High tolerance and rare queries → long context may be acceptable.

5. How fast must responses be?

   • Low latency → minimize heavy retrieval or huge contexts. Cache where possible.

6. How will you debug answers?

   • If you need traceability, use RAG. It gives source links and a clearer pipeline.

Case examples (short)

• One-off report summary. Upload the full report and use a long-context model. No external memory needed.

• Customer support with fast policy changes. Use RAG so answers cite the latest policy docs.

• Healthcare assistant with patient history. Combine episodic memory for patient facts and RAG for current medical research.

Final advice

Be pragmatic. Pick the right tool for the job. Use RAG when you need accuracy, currency, and traceability. Use long contexts for deep, one-off reads. Use selective episodic memory for personalization across sessions.

Design for cost and clarity. Keep the context lean. Validate sources. And make sure the system can explain where its answers come from.

That approach makes your LLM system accurate, efficient, and easier to maintain.