All 127 posts in date order, newest first. ← back to topics
Stream the agent's reasoning and tool calls to the UI as they happen. The user sees "thinking about X, calling tool Y, got result Z, now answering..." — dramatically better UX than waiting for the final answer.
The smallest possible multi-tool agent. The loop is 30 lines of Go and shows exactly what an "agent" is — there's no magic, just a structured back-and-forth between the LLM and a set of tools until the model says stop.
The tool-calling dance: the LLM emits a structured tool call → application runs the tool → application appends the result → the LLM uses it in the next turn. Two phases. Everything else is detail.
A simple RAG pipeline embeds documents one at a time. The performant version batches the embeddings, parallelises the chunks, and caches the responses. Throughput goes up 5-10×.
Tie all the RAG pieces together into one interactive REPL. Type a question, see the retrieval, see the answer, ask follow-ups. The shape of every "chat with your docs" demo.
When RAG gives wrong answers, the problem is usually retrieval, not the LLM. The example isolates the retrieval step so you can see exactly what chunks come back for a given query, with what scores, and tune K and the similarity threshold accordingly.
Ingest → embed → store → retrieve → answer. The full pipeline applied to Bill Kennedy's Go notebook. The result: a system that answers "how do channels work?" with quotes from the source material.
The ingestion step that turns a corpus into a vector database. Chunk the source, embed each chunk, store with metadata. The pre-work without which RAG is impossible.
pgvector adds vector similarity to Postgres. The example shows the schema, the indexes, the query, and what an ANN index buys you over a brute-force scan.
Side-by-side comparison: ask the LLM a domain question with no context, then ask with retrieved context. The without-RAG answer is plausible nonsense. The with-RAG answer is correct. The example that motivates everything else in the course.