Technical Execution & AI Orchestration
Trigger words: "RAG," "fine-tune," "latency," "multiple tools," "agent workflow"
First move, before answering
- Find the noun — the system/workflow, not the underlying model.
- Is the underlying knowledge static or constantly changing?
- What matters more, speed or accuracy? Read it from the prompt.
- What's the one undefined word? Usually "orchestration" or "autonomously."
- Load one sentence: "I'll assume the knowledge changes often enough that fine-tuning would go stale. Sound right?"
Framework: TASK → DATA → METHOD → CHAIN → BUDGET
Name exactly what the model does, retrieval, generation, classification, reasoning. Be specific.
Static or frequently changing knowledge, the biggest input to METHOD.
RAG, fine-tuning, prompt engineering, or hybrid, justified against DATA.
If multiple tools/steps are involved, describe the sequence and name the failure mode if one link breaks.
State the latency/cost constraint and one concrete lever, smaller model, caching, streaming.
Worked example
“An LLM feature takes 15 seconds to return an answer, causing users to drop off. How do you trade accuracy for lower latency?”
Opening line: “Give me a moment to think through this.”
Is this answering questions from a document, or generating longer free-form content? → Answering support questions from a knowledge base.
Retrieval-based question answering, pulling the right fact and phrasing it clearly.
I'll assume this knowledge base updates regularly, since support content changes often.
RAG is likely already the approach, not fine-tuning, since we want current answers. The latency problem is probably the implementation, not the approach.
The 15 seconds likely comes from three serial steps: embedding the query, retrieving from the vector store, generating the answer. Any slow step makes the whole chain wait.
I'd stream the answer as it generates so users see something in under a second, and use a smaller, faster model for retrieval, reserving the larger model for final generation.
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