LLM Regression & Eval Suite
We build an automated evaluation harness and CI regression gate for your production AI feature. Never deploy a prompt tweak that breaks downstream user workflows.
Fixed-price pilot from $2,000 • 2-week delivery • Strict NDA & sanitized datasets
THE REALITY OF SHIPPING LLMS
The Prompt Tweaking Trap
Invisible Regressions
You adjust a system prompt to fix an edge case in billing summarization, but accidentally degrade formatting across customer support classification.
No CI Gate
Frontend and backend code have automated unit tests. AI features rely on "looks good to me" vibes in playground tabs before merging.
Silent Model Drift
OpenAI, Anthropic, or Google update upstream weights or deprecate endpoints. Outputs subtly shift, and you only find out when users complain.
WHAT WE BUILD
Complete 6-Part Deliverable Suite
Everything needed to run regression testing in CI without recurring SaaS subscriptions.
1. Curated Golden Dataset
50 to 200 real and synthetic test fixtures covering baseline happy paths, schema edge cases, prompt injection attempts, and boundary inputs.
2. Deterministic Schema Asserter
Fast, zero-cost deterministic assertions: JSON schema adherence, enum validation, forbidden regex patterns, and token latency budgets.
3. Calibrated Scoring Rubric
Dual-layer evaluation combining deterministic pass/fail flags with an LLM-as-judge scoring rubric aligned to your product quality definition.
4. CI Regression Gate
Ready-to-use GitHub Actions or GitLab CI workflow that runs on pull requests and blocks merging when overall score drops below your threshold.
5. Baseline Benchmark Report
Full evaluation report establishing current baseline accuracy, hallucination frequency, latency percentiles, and unit test logs.
6. 60-Minute Team Handoff Call
Live session walking your engineering team through test execution, adding new fixtures, and adjusting scoring criteria.
Clear Scope Boundaries
To guarantee 2-week turnaround and predictable pricing, we are explicit about what is out of scope:
- Rewriting customer application features
- Direct prompt engineering or re-architecting
- Formal enterprise security compliance certification (SOC2 / ISO)
HOW IT WORKS
The 2-Week Implementation Process
Failure Mode Mapping & Scope
We audit your target LLM feature, inputs, JSON schemas, and previous edge case failures. We identify latency budgets, unacceptable errors, and critical failure modes.
Golden Dataset Synthesis
We construct a synthetic and sanitized production dataset (50 to 200 cases) covering typical usage, adversarial edge cases, formatting stress tests, and boundary inputs.
Deterministic Checks & Scoring Rubric
We implement rigorous schema validators, type assertion checks, and cost-efficient LLM-as-judge prompts with deterministic scoring rubrics and reference benchmarks.
CI Regression Gate Integration
We wire the eval harness directly into your GitHub Actions or existing CI pipeline so PRs fail if quality scores drop or breaking hallucinations appear.
Baseline Report & Team Handoff
We generate your baseline benchmark score, document the harness, and conduct a 60-minute handoff call so your engineers can extend test cases independently.
ENGAGEMENT OPTIONS
Simple, Predictable Packages
LLM Regression & Eval Suite
2 weeks turnaround
We build an automated evaluation harness and CI regression gate for your core LLM pipeline. Catch hallucinations, formatting breaks, and prompt regressions before deploying to users.
Included Deliverables:
Drift & Regression Monitoring
Continuous monitoring
Monthly evaluation maintenance and dataset refreshes as upstream LLM provider models, system prompts, or customer usage patterns shift.
Monthly Coverage:
FAQ
Frequently Asked Questions
How do you keep our customer and prompt data secure?
We operate under a strict NDA. We can generate completely synthetic test cases that mimic real distributions, or work directly within your private staging environment using sanitized fixtures. We never train external models on your proprietary prompts or logs.
Why do we need a CI eval gate instead of manual testing?
Prompt and model changes have unpredictable blast radiuses: tweaking a prompt for one edge case frequently causes regressions in three others. A CI gate runs automated test suites on every pull request, flagging regressions in seconds without engineering toil.
Which LLM providers and frameworks do you support?
We build framework-agnostic suites compatible with OpenAI, Anthropic, Gemini, Mistral, and local/open-source models (vLLM, Ollama), wired through standard CI tools like GitHub Actions, GitLab CI, or CircleCI.
What happens if we need prompt fixes or application changes?
Our core deliverable is the measurement and regression gate. We identify root causes and quantify failure modes. If your team needs hands-on prompt redesign or architectural remediation, we can scope that separately.
How does the free Eval Readiness Check work?
You submit your feature description and sample input/output schema. Within 48 hours, we provide an async written report detailing your primary failure vectors and a concrete evaluation rubric recommendation.
Stop Flying Blind with LLM Features
Get an automated CI gate that catches hallucinations and schema drops before your customers do.