Tag: rag

Part one is the practical on-ramp: a transcript-search use case, a RAG-ish starting point, and the moment DSPy becomes more than a nicer prompt template.

Good to keep because it explains why declarative signatures and modules matter before jumping into optimizer results. The concept lands better when the first problem is messy enough to deserve it.

The thesis is deliberately provocative, which makes it useful. RAG is not simply dead, but the old chunk-embed-rerank default is under pressure from larger contexts, agentic retrieval loops, and document-native workflows.

I would keep this as a debate starter, not a doctrine. The production question is still which retrieval shape gives the model the right evidence at the right cost with inspectable failure modes.

This is a useful framing link because it shifts the work from clever prompts to the operational discipline of selecting, shaping, and evaluating context. That maps directly to agent systems, RAG, long-context workflows, and product features that must explain why the model saw what it saw.

The caution is that "context engineering" can become a new vague label. It is only valuable when it produces inspectable inputs, measurable outcomes, and clearer failure modes.

This is a Google-side reference for the embedding layer behind retrieval and context engineering. It is worth keeping because the site is already leaning into Vertex AI, Gemini, and production-grade AI systems, where embeddings are infrastructure rather than a demo detail.

The useful question is not whether embeddings are good in isolation. It is whether the retrieval loop improves task success, preserves source traceability, and handles stale or missing context gracefully.