TL;DR: If your LLM app “mostly works” in production, you might already be paying hidden costs in latency, cloud bills, and user trust. Before tuning your prompt one more time, run this 30-minute audit.

This is a developer-focused companion to the original article on whether you need an LLM audit.

If you want broader context on how GenAI-specific audit differs from general AI system audit, check out this companion piece: https://optyxstack.com/llm-audit/genai-audit-vs-ai-system-audit

The Real Problem: “Mostly Works” in Production

In early demos, everything looks fine.

In production, you start hearing:

“Sometimes it makes things up.”
“It works… but not for me.”
“Why did our AI bill double?”

An LLM audit isn’t a strategy slide.

It’s about establishing:

Baseline performance
Failure mode mapping
Prioritized fixes

If you recognize 3 or more of the signs below, you’re likely operating blind.

9 Signs Your LLM App Needs an Audit

1. Average Quality Looks Fine — But Users Still Complain

Your internal tests say 8/10.

Support tickets say “wrong answer.”

What’s happening?

Usually:

Long-tail intents are failing
Certain languages or cohorts break
Edge-case documents cause hallucinations

What to check

Segment metrics by intent, language, document type, tenant tier
Don’t rely on a single aggregate score

2. Your RAG System Answers Confidently — But Incorrectly

This is rarely a “prompt problem.”

Common root causes:

Low retrieval recall
Stale documents
Context assembly errors
Chunking mismatches

What to check

Retrieval recall proxy
Citation validity sampling
Document freshness
Chunk overlap quality

3. Costs Are Rising — But Nothing Major Changed

Symptoms:

Bill increases month over month
No obvious product change

Typical hidden drivers:

Infinite retries
Tool loops
Prompt bloat
Overusing expensive models for easy queries
Reranking on every request

What to measure

Cost per successful task (not per request)
Cost breakdown per stage (retrieval, generation, tools)

4. P95 Latency Hurts — But You Don’t Know Where

P50 looks fine.

P95/P99 kills UX.

Without stage-level tracing, you’re guessing.

Break latency down into:

Retrieval
Rerank
Tool calls
Generation (TTFT + total)
Queueing
Retries

You can’t optimize what you don’t isolate.

5. You Can’t Tell If Failures Are Retrieval, Generation, or Tooling

If every bug results in “let’s tweak the prompt,” you lack failure classification.

You need to label:

Retrieval failure
Context construction failure
Generation hallucination
Tool execution error
Policy/validation rejection

Without this, you’re debugging blind.

6. You Ship Prompt Changes Without Regression Gates

You improve intent A.
You break intent B.

Classic pattern.

If you don’t have:

A golden dataset
Automated evaluation
Release gating

Then you’re running production experiments without guardrails.

7. Your Agent or Tool Calling Is “Unstable”

Symptoms:

Sometimes works.
Sometimes loops.
Sometimes calls the wrong tool.
Sometimes silently fails.

You should track:

Tool success rate
Loop rate
Tool latency distribution
Structured validation before execution

Agents without observability become chaos machines.

8. You’re Stuck in Prompt Optimization Hell

If your workflow looks like:

Modify prompt
Deploy
Hope
Repeat

Then you’re treating symptoms, not system design.

Real improvements often come from:

Better routing
Context pruning
Caching
Validation layers
Retrieval redesign

Prompt tuning is often the last 10%, not the first 90%.

9. Enterprise or Compliance Is Blocking You

You’re asked:

What do you log?
How do you handle PII?
How do you prevent prompt injection?
Can data be exfiltrated via tools?

And the answer is… unclear.

That’s not just technical debt.

That’s business risk.

30-Minute Self-Assessment (Copy/Paste)

Score each item 0–2:

0 = Not implemented
1 = Partial / inconsistent
2 = Solid and actively used

A) Measurement

We track task success with a defined metric (not just thumbs up/down)
We measure citation validity / groundedness
We track cost per successful task
We track P95 latency and TTFT separately
We segment metrics by cohort/intent

B) Diagnosis

We can label failures by system layer
We maintain a top failure taxonomy
We can reproduce issues using stored traces

C) Control

Prompt/model changes go through regression testing
Outputs are validated (schema/policy/citations) before user/tool execution
We have measurable routing policies (fast vs smart model)
We have a defined security posture (PII, injection mitigation)

Score Interpretation

0–10 → You’re operating blind. Audit will likely pay off fast.
11–16 → You have pieces, but you’re treating symptoms.
17–24 → You’re mature, but audit can unlock cost/performance gains.

Minimum Logging Schema (You Need This)

If you log nothing else, log enough to answer:

Where did it fail?
Where did the time go?
Where did the money go?

At minimum:

trace_id
routing.selected_model
retrieval.latency_ms
retrieval.results[] (doc_id, chunk_id, score, last_updated)
context_build.final_context_tokens
generation.usage (input_tokens, output_tokens, cached_tokens)
generation.ttft_ms
tools.calls[] (tool_name, latency_ms, status)
validation.result
outcome.success_label
failure_layer
cost.breakdown_by_stage

No logs = no real diagnosis.

A 1–2 Day Practical Plan

If you don’t want a formal audit yet, do this:

Run the 30-minute self-assessment.
Collect 50–100 representative production queries.
Enable stage-level tracing.
Label 20–30 failure cases manually.
Fix 1–2 high-ROI issues (retry loops, prompt bloat, routing inefficiency, context overflow).

After this, you’ll know:

Whether prompt tuning is the real issue
Whether retrieval is broken
Whether you’re overspending
Whether you need a deeper audit

Final Thought

If your LLM system “mostly works,” that’s not success.

It’s often an expensive illusion.

Before tuning another prompt, measure the system.

You might discover the real bottleneck isn’t the model at all.

LLM Audit for Developers: A 30-Minute Self-Check Before You Tune That Prompt Again