Summary: For high-volume production features with strict accuracy, latency, cost, and explainability needs, a mostly deterministic pipeline with LLM steps beats an autonomous agent: no tool-choice mistakes or back-and-forth, and every output can be explained to a customer. Start by defining KPIs, give each LLM step the minimal context it needs, ask for reasoning plus confidence, validate claims against ground truth, and route the cases the pipeline can’t handle to fallback strategies.
Key ideas
- Define KPIs before experimentation, not after a naive proof of concept (langtalks—66-scaling-llmops, avi-lumelsky).
- Minimal context: for CVE enrichment, not the whole codebase (unscalable) but the patch diff plus all public references, just as a security researcher reads the fix (langtalks—66-scaling-llmops).
- Ask for reasoning and a 0–1 confidence; tune the threshold on larger experiments; never present LLM output as gospel. Check that the claimed function exists in the customer’s environment and version to cut false positives (langtalks—66-scaling-llmops).
- Explainability matters for customers: “the diff changed authenticate_user and the CVE describes an auth bypass” (langtalks—66-scaling-llmops).
- Funnel of strategies: the pipeline handles X%, and the rest goes to a fallback (skip for now, run an agent and tell the user it’ll take longer, or a stronger model on the same pipeline) (langtalks—66-scaling-llmops).
- Rule-based pre-filters (regex, graphs) can come from domain experts or from Claude Code mining labeled data (langtalks—66-scaling-llmops).
- Language-agnostic tooling: an OpenAI-compatible internal endpoint (AWS Bedrock Access Gateway) let Go, TypeScript, and Python teams use any SDK; LLM calls live inline in existing services, not separate “AI microservices” (langtalks—66-scaling-llmops).
- A client SDK wraps prompts with their context-building logic and versions them, so swapping a prompt version is a one-line change and the prompt has one source of truth (langtalks—66-scaling-llmops).
- Strategy flavors on one engine (“accurate” vs “fast” via config, e.g. skipping extra validation and using faster models) let a new channel (voice) reuse the same evals and feedback loop instead of forking the system (langtalks—61-voice-agents).
- Pipeline steps that are really segmentation or classification can move to fine-tuned SLMs (e.g. splitting a bundled PDF, then routing each document) (langtalks—59-slms).
Disagreements & open questions
- Use an off-the-shelf proxy (features, but a dependency) or build a lean internal one (now easy with Claude Code)? (langtalks—66-scaling-llmops)
Takeaways
- Define KPIs for an AI feature before the first experiment (langtalks—66-scaling-llmops)
- Prefer a pipeline with reasoning + confidence + ground-truth validation over an agent for high-volume features (langtalks—66-scaling-llmops)
- Version prompts together with their context-building code in a shared client SDK (langtalks—66-scaling-llmops)
Related
llm-evals · model-selection · decision-classifiers · ai-gateway · voice-agents · small-language-models