Building a Memory System for My AI Code Generator

AKSHAYAA VEENA — Sat, 20 Jun 2026 08:46:21 +0000

When I first built my AI-based code generator, I handled memory the same way most beginner LLM projects are designed. I had a simple temporary buffer that stored the ten most recent prompts. This created a major problem: once the conversation exceeded ten prompts, the oldest prompt would be discarded. I noticed this when I tried asking the assistant about a login form we had discussed much earlier in the conversation. Even though it was an important part of the project, the model had no memory of it because those messages had already been removed.

This made me realize the importance of context management. I needed to manage both recent conversations and information from much earlier in the session. To solve this, I enhanced the memory layer into three major parts.

The first part is the short-term buffer, which stores the latest four prompts.

The second part is a rolling summary. When the conversation becomes too long, I don't simply delete older messages. Instead, I compress them into a short summary that captures the important decisions and progress made so far. This keeps the overall story of the project available without continuously increasing token usage.

The third part is vector memory. Every user message gets converted into an embedding and stored. Whenever a new prompt arrives, I create an embedding for that prompt and search through previous messages to find the ones that are most similar in meaning. This allows the assistant to bring back meaningful information from much earlier in the conversation, even if it happened dozens of messages ago.

My favorite part of the project is actually something users can see. I built a Context Inspector that shows exactly what the memory system is doing in real time. Instead of treating memory as something hidden behind the scenes, it displays the current summary, the recent conversation buffer, and any messages retrieved from vector memory. Being able to watch those pieces work together makes the entire architecture much easier to understand.

There is still plenty I want to improve. Streaming responses would make generations feel faster, proper authentication would replace the temporary session system I'm currently using, and eventually the brute-force vector search will need to be replaced with a more scalable indexing approach.

If you're building something similar or have opinions on rolling summaries vs pure vector recall — I'd love to hear how you're approaching it.

DEV Community: AKSHAYAA VEENA

Building a Memory System for My AI Code Generator