Model & prompt benchmarking
Compare candidate models, prompt versions, and configurations against the same task-specific test set. Track accuracy or rubric scores by use case, including difficult inputs and known regressions.
AI model benchmarking.
Evidence for every decision.
QATitans evaluates AI models, LLM applications, RAG systems, and AI agents. We turn product expectations into repeatable tests, measurable criteria, and findings your engineers can investigate.
WHAT WE EVALUATEBeyond a good demo.
Beyond a good demo.
Into real behavior.
Benchmark the tasks your users need to complete. Evaluate quality alongside failure behavior, response time, and the cost of serving those tasks.
LLM response evaluation
Check instruction following, answer relevance, factual correctness against reference evidence, and structured output validity. Review unsupported claims, ambiguous questions, and appropriate refusal behavior.
RAG pipeline evaluation
Assess retrieval-augmented generation at both stages: whether retrieval finds the right evidence, and whether the answer uses it correctly. Examine retrieval precision and recall, groundedness, and citation support.
AI agent & workflow testing
Test task completion, tool selection, arguments, and multi-step behavior. Exercise permissions, failed tool calls, timeouts, retry limits, and human handoffs with trace-level evidence.
Safety & adversarial scenarios
Probe prompt injection, unsafe output, sensitive-data disclosure, and inconsistent policy behavior within an agreed test scope. Record reproducible inputs and review findings with the product team.
Latency, reliability & cost
Measure end-to-end latency, time to first token for streaming responses, token usage, request failures, and estimated inference cost under documented pricing assumptions.
A REPEATABLE BENCHMARKSame conditions.
Same conditions.
Meaningful comparisons.
A benchmark starts with the task, the dataset, and the scoring rules. We document what changed between runs so your team can distinguish an actual quality improvement from differences in inputs, configuration, or evaluation.
Build the evaluation set
Version representative examples, expected outputs, edge cases, and domain-specific rubrics. Keep a held-out test set separate from prompt tuning.
Record the conditions
Capture model identifiers, prompts, generation settings, retrieval configuration, and evaluator versions. Establish a baseline before comparing changes.
Combine checks & review
Use deterministic checks, task metrics, calibrated model-based grading, and human review. Repeat selected cases to measure output variability.
Report the trade-offs
Compare results by task and input category. Review quality regressions alongside latency, errors, and cost before agreeing release criteria.
| Evaluation area | Example signals | Evidence |
|---|---|---|
| Task quality | Accuracy where references exist, rubric scores, instruction and schema adherence | Per-case inputs, outputs, scores, reasons, and reviewed examples |
| RAG quality | Retrieval precision and recall, groundedness, citation support | Retrieved context, source references, and unsupported claims |
| Agent behavior | Task completion, tool and argument correctness, recovery behavior | Execution traces, tool calls, state transitions, and failure paths |
| Operational quality | Latency percentiles, request failures, token use, estimated cost | Run configuration, measurements, workload assumptions, and error records |
DELIVERABLES YOUR TEAM CAN REUSEFrom benchmark
From benchmark
to regression suite.
Agree the datasets, metrics, review process, and reporting scope at the start of the engagement.
- Versioned evaluation datasets and scoring rubrics
- Reusable benchmark scripts and execution instructions
- Comparison reports with results by task and input category
- Failure traces and prioritized findings with reproduction steps
- Human-review notes and an agreed regression test set
Questions about AI evaluation
Can one score tell us which model is best?
A score depends on the dataset, task, grading method, and runtime conditions. We compare the measures relevant to your product and inspect important failure categories alongside the overall result.
How do you handle model-based grading?
We document the rubric and evaluator version, calibrate against human-reviewed examples, and review disagreements and borderline cases. A model-based judge can make mistakes, so its output is treated as evaluation evidence rather than ground truth.
What happens if an evaluation call fails?
Timeouts, API errors, and invalid evaluator responses are recorded separately from quality failures. Reports show the attempted, scored, failed, and retried counts so missing evaluations do not silently inflate a pass rate.
Does a grounded answer mean the answer is correct?
Groundedness measures support in the retrieved context. The context itself may be incomplete or wrong, so factual correctness and retrieval quality need their own checks where suitable references are available.
MAKE AI QUALITY MEASURABLEWhat should your AI
Discuss AI evaluation