103 posts · Page 7 of 9. ← Blog
Embedding a question and embedding an answer often produce different vectors. HyDE generates a hypothetical answer to the question, embeds *that*, and retrieves on it. Retrieval quality goes up disproportionately.
Naive RAG retrieves on every query. Self-RAG decides whether to retrieve. CRAG decides whether the retrieved content is good enough or needs corrective retrieval. Two papers; both worth implementing.
An Indian banking deployment needs to handle Hindi, Marathi, Tamil, Bengali, and English in the same retrieval pipeline. Bhashini (the government's language stack) plus cross-lingual embeddings make it tractable.
Anthropic's A2A spec standardises how agents talk to other agents (not just tools). The Go client is small; the conceptual shift is what matters.
Google publishes a 12-pattern taxonomy for agent design. Most of them have direct corollaries in production code; one or two are best ignored. The mapping I've used.
A saga is fine when every step succeeds. The interesting code is what runs when step 3 of 5 fails and you have to undo 1 and 2 in the right order. The patterns I use.
Not every query needs the production agent. A cost-aware dispatcher decides whether to route to the cheap-and-fast agent or the expensive-and-thorough one. Same UX, dramatically lower bill.
Two agents can do the same job. One takes 200ms; the other takes 5 seconds. Pick by user-facing SLO, not by which agent is "better." The dispatcher pattern.
Postgres over the latest vector DB. Go stdlib over the framework du jour. Single binary over Kubernetes operator. The choices that bore reviewers and delight on-call engineers.
An agent that doesn't declare a tier defaults to Prototype, not Production. The flag is the code; the culture is what enforces "new code is not production until someone says so."
Go's embed.FS bundles files into the binary at compile time. The pattern collapses what would be a multi-artefact deploy into one binary. Three places it pays back daily.
GOMEMLIMIT tells the Go runtime to keep memory below a soft cap by running GC harder when it's close. For containers with hard memory limits, this prevents OOM kills. The setting every Go service in K8s should have.