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Building Reliable LLM Features: From “Cool Demo” to Production Signal
Dec 20, 2025 · 2 min · Project
Building Reliable LLM Features: From “Cool Demo” to Production Signal
Most LLM projects start the same way: a prompt, a model, a surprisingly good response, and immediate temptation to ship. The gap between that and something a team can actually depend on is evaluation, observability, and repeatability.
The problem: “Works on my prompt”
LLMs fail in ways classic software rarely does. A change that improves one prompt can degrade another. A retrieval tweak can increase recall but quietly introduce hallucinated details. A model update can alter tone, specificity, or refusal behavior. Without guardrails, you’re debugging vibes.
What I care about is turning LLM output into a measurable artifact:
- Is the answer grounded in the provided context?
- Did the system use the relevant source chunks?
- Is the answer complete for the asked question?
- Does it behave consistently across versions?
Approach: evaluate like software, not like copywriting
The structure I like is:
-
Define the task contract
- Inputs: user prompt + retrieved context (if any)
- Output constraints: structure, length, allowed claims, references
- Failure modes: hallucination, irrelevance, omission, unsafe content
-
Create a small, high-quality test set
- Realistic queries, not toy examples
- Includes “hard negatives” (similar docs, misleading context)
- Tracks expected citations or expected key points
-
Automate evaluation
- Faithfulness / grounding checks (answer supported by context)
- Relevance checks (answer addresses the question)
- Retrieval metrics (did the pipeline fetch what it should?)
- Regression detection across model/prompt changes
-
Treat prompts like code
- Version them
- Run evaluations in CI
- Require a “pass” threshold before merging
Practical details that matter
- Caching: evaluation can be expensive; cache model outputs per commit.
- Dataset drift: refresh test cases as product usage evolves.
- Judge reliability: judge-LLMs need calibration; use multiple signals, not one metric.
- Traceability: store: prompt version, model version, retrieved docs, output, scores.
The takeaway
I like building systems where “intelligence” is observable, testable, and maintainable. The value isn’t only the model response; it’s the engineering that makes the response dependable.
Building Reliable LLM Features: From “Cool Demo” to Production Signal
Most LLM projects start the same way: a prompt, a model, a surprisingly good response, and immediate temptation to ship. The gap between that and something a team can actually depend on is evaluation, observability, and repeatability.
The problem: “Works on my prompt”
LLMs fail in ways classic software rarely does. A change that improves one prompt can degrade another. A retrieval tweak can increase recall but quietly introduce hallucinated details. A model update can alter tone, specificity, or refusal behavior. Without guardrails, you’re debugging vibes.
What I care about is turning LLM output into a measurable artifact:
- Is the answer grounded in the provided context?
- Did the system use the relevant source chunks?
- Is the answer complete for the asked question?
- Does it behave consistently across versions?
Approach: evaluate like software, not like copywriting
The structure I like is:
-
Define the task contract
- Inputs: user prompt + retrieved context (if any)
- Output constraints: structure, length, allowed claims, references
- Failure modes: hallucination, irrelevance, omission, unsafe content
-
Create a small, high-quality test set
- Realistic queries, not toy examples
- Includes “hard negatives” (similar docs, misleading context)
- Tracks expected citations or expected key points
-
Automate evaluation
- Faithfulness / grounding checks (answer supported by context)
- Relevance checks (answer addresses the question)
- Retrieval metrics (did the pipeline fetch what it should?)
- Regression detection across model/prompt changes
-
Treat prompts like code
- Version them
- Run evaluations in CI
- Require a “pass” threshold before merging
Practical details that matter
- Caching: evaluation can be expensive; cache model outputs per commit.
- Dataset drift: refresh test cases as product usage evolves.
- Judge reliability: judge-LLMs need calibration; use multiple signals, not one metric.
- Traceability: store: prompt version, model version, retrieved docs, output, scores.
The takeaway
I like building systems where “intelligence” is observable, testable, and maintainable. The value isn’t only the model response; it’s the engineering that makes the response dependable.