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Designing LLM Features That Don’t Break in Production

Most LLM failures in products aren’t model failures—they’re interface, data, and lifecycle failures. This post lays out a practical blueprint for building LLM features that behave predictably, degrade gracefully, and are testable end-to-end.

Overclock Team10 min read
Designing LLM Features That Don’t Break in Production

LLMs are easy to demo and surprisingly easy to ship—until you need consistent behavior under real user traffic.

If you want an LLM feature that doesn’t break in production, treat it like any other distributed system component: define its contract, control its inputs, measure its outputs, and engineer fallbacks.

Below is a pragmatic approach you can apply whether you’re shipping chat, extraction, summarization, or agentic workflows.

1) Start with the contract: what must the model do?

Before prompts, pick a concrete I/O contract:

  • Input shape: what fields are required? (user text, context snippets, account metadata)
  • Output shape: JSON schema, Markdown, or a typed internal object
  • Quality constraints: what must be correct vs. “nice to have”
  • Latency and cost budget: P50/P95 and max tokens
  • Refusal boundaries: what requests should be blocked or rerouted

If you can’t write the contract down, you can’t test it.

A rule that works

> If downstream code branches on the model output, the output must be structured.

A free-form paragraph is fine for UI display, but brittle for automation. When automation matters, use a JSON schema and validate it.

2) Make the prompt an artifact, not a string

Prompts drift. Engineers edit them in-line. A/B variants get lost. Make prompt changes reviewable.

Treat a prompt like code:

  • Version it alongside the service
  • Add a changelog and owner
  • Include examples (few-shot) and counterexamples
  • Encode system boundaries explicitly (what tools exist, what not to do)

A practical prompt structure:

  1. System: role + safety/format constraints
  2. Developer: task definition + success criteria
  3. Context: retrieved snippets + metadata
  4. User: request
  5. Output: schema and strict formatting rules

3) Constrain inputs: retrieval and context hygiene

A large share of “hallucination” is just bad context.

Retrieval checklist

  • Deduplicate snippets and sort by relevance
  • Use short, atomic chunks (avoid multi-topic blocks)
  • Track source IDs so you can debug “why did it say that?”
  • Include timestamps and version identifiers for docs

Context hygiene rules

  • Keep context under a known token ceiling; don’t feed the entire universe
  • Don’t mix authoritative and speculative sources without labeling
  • If the context is empty or low-confidence, avoid answering confidently

A simple pattern: attach a context_confidence value and gate behaviors (e.g., require citations when confidence is low).

4) Design for graceful failure: fallbacks and “safe defaults”

LLMs fail in predictable ways: malformed outputs, tool misuse, policy collisions, latency spikes, and partial retrieval.

Plan for failure up front:

  • Schema repair loop: one retry with explicit “fix JSON to match schema”
  • Tool fallback: if the agent fails to call a tool, run the tool deterministically
  • Answer fallback: if uncertain, return a short clarification question
  • Product fallback: show a baseline search result or static guidance

Don’t “retry until it works.” Put a hard cap on retries and log the reason.

5) Evaluate the feature, not the model

Teams often ask “Is model X better than model Y?” The real question is:

> Does this feature meet the product’s quality bar under realistic inputs?

Build evaluations around user tasks and failure modes.

A minimal evaluation stack

  1. Golden set: 50–200 curated examples of real tasks
  2. Scoring:
  • Format validity (schema)
  • Task success (heuristic or labeled)
  • Factuality / citation correctness
  • Safety violations
  1. Regression gate: block merges when key metrics drop

For early-stage products, start with simple checks:

  • JSON validates?
  • Contains required keys?
  • Includes citations when expected?
  • Passes a small set of human-reviewed examples?

Then add deeper quality labels over time.

6) Add observability you can act on

If your logs are “prompt + response,” you will struggle to debug. Log at the right abstraction level.

What to capture per request:

  • Prompt version, model ID, temperature, max tokens
  • Retrieval stats (top-k, source IDs, similarity scores)
  • Tool calls (names, arguments, duration, success/failure)
  • Output validation result and repair attempts
  • Cost and latency
  • User-visible outcome (success, fallback path)

Production dashboards that matter

  • Output schema failure rate
  • “No answer” / clarification rate
  • Tool failure rate by tool
  • Latency P50/P95 by model and route
  • Cost per successful outcome

The goal: identify whether failures come from retrieval, the model, tool APIs, or orchestration.

7) Separate “thinking” from “doing”

Agentic workflows become fragile when the model both reasons and executes with no guardrails.

A robust architecture:

  • Planner (LLM): proposes actions in a constrained plan format
  • Executor (deterministic code): validates and runs allowed actions
  • Verifier (LLM or rules): checks outputs against constraints

Even if you keep it simple, add a deterministic executor layer that rejects unsafe or malformed tool calls.

8) Use routing: not every request needs the best model

Route requests by complexity and risk:

  • Low-risk summarization → cheaper model
  • High-impact actions (send email, change records) → stronger model + stricter checks
  • Low context confidence → ask clarifying questions or do retrieval again

Routing gives you:

  • Lower cost
  • Better latency
  • More predictable behavior

9) Tighten the loop: human feedback and targeted datasets

When you see repeated failures, don’t “prompt harder.” Turn failures into data.

A practical loop:

  1. Capture problematic inputs and outputs with metadata
  2. Label root cause (retrieval, formatting, policy, misunderstanding)
  3. Add to a hard-case set
  4. Update prompt, retrieval, or tool contract
  5. Re-run evaluation gate

If you’re doing fine-tuning, keep it targeted: teach formatting and task conventions, not broad “knowledge.”

10) A reference checklist before you ship

Before an LLM feature is “production-ready,” confirm:

  • Output schema validation + one repair attempt
  • Deterministic fallbacks for no-context and tool failures
  • Task-level evaluation set and regression gate
  • Retrieval is auditable (source IDs) and context is bounded
  • Observability for latency, cost, tool success, and schema failures
  • Routing strategy for cost/latency control
  • Clear refusal and safety boundaries

Closing

LLMs are powerful, but they’re also stochastic dependencies with evolving behavior. The teams that ship reliably treat LLM features as systems: contracts, validations, fallbacks, and measurable outcomes.

If you do that, you’ll spend less time chasing weird edge cases—and more time building capabilities users can actually trust.