AI & Automation
Retrieval-Augmented Generation (RAG) Systems
RAG is the technique behind every grounded AI feature on this site's solutions cluster — instead of relying on a language model's general training, relevant chunks of your own data are retrieved first (usually via vector similarity search) and passed into the model's context, so its answer is based on what you actually have rather than what it might have learned generally.
I build the full pipeline: chunking your source content sensibly, generating and storing embeddings, retrieval logic, and the prompt structure that combines retrieved context with the user's question.
How a RAG Pipeline Works
- Source content is split into chunks small enough to be individually relevant but large enough to retain context
- Each chunk is converted into a vector embedding and stored in a vector database
- A user's question is embedded the same way and compared against stored chunks to find the most relevant ones
- Retrieved chunks are inserted into the prompt as context alongside the user's question
- The model generates a response grounded in that retrieved context, ideally with citations back to the source
Where Quality Actually Comes From
The model itself matters less than the retrieval step — if the wrong chunks get retrieved, even the best model produces a confidently wrong answer. Chunking strategy, embedding quality, and retrieval tuning are where most of the real engineering effort and iteration happen.
Frequently Asked Questions
What's the difference between RAG and fine-tuning?add
Fine-tuning changes the model's weights based on training examples, which is expensive and needs to be redone as data changes. RAG keeps the model unchanged and retrieves fresh, current data at query time — cheaper, faster to update, and generally the better fit for grounding answers in data that changes regularly.
What vector database do you use?add
Depends on scale and existing infrastructure — options range from a Postgres extension (pgvector, which pairs well with a Supabase setup) to dedicated vector databases for larger-scale needs. I choose based on what fits the project's existing stack.
How do you prevent irrelevant chunks from being retrieved?add
Tuning chunk size, embedding model choice, and retrieval parameters (like how many chunks to retrieve and any relevance threshold) against real test queries — this tuning is iterative and specific to the actual content being searched.