Pipeline debugging
Trace failures across prompts, tools, APIs, context windows, retries, secrets, and model calls instead of blaming the model first.
AI Engineering Optimization
Fix the AI pipeline, not just the prompt.
Give NEO a messy RAG app, slow agent, failed fine-tune, or expensive model workflow. It reads the system, builds the eval, finds the bottleneck, tests fixes, and reports the numbers.
neo task "Audit the support-bot RAG pipeline.
Measure answer quality, retrieval hit rate, latency, and cost.
Try retrieval fallback, prompt grounding, and cheaper model options.
Write the report to reports/rag-audit.md."+19%quality after RAG fixes
-79%session cost after model sweep
7.88final judged quality score
Watch Setup
See NEO setup and fixes in action.
We're setting up NEO with our account in VS Code IDE and giving it a task to audit our customer support agent for optimizations and quality enhancements.
Problem
AI failures rarely live in one file.
A bad answer might be a retrieval issue. A slow model might be a context packing issue. A fine-tune might look successful while the downstream task gets worse.
NEO treats AI work like engineering work: inspect the implementation, define a baseline, isolate the cause, run experiments, and leave a report someone can review.
Where NEO Helps
The AI engineering work between idea and production.
NEO can take on the messy work around models, data, retrieval, prompts, costs, and production behavior.
RAG and retrieval
Inspect chunking, embedding quality, thresholds, reranking, source grounding, and zero-context failures.
Evaluation harnesses
Turn vibes into repeatable scorecards: LLM-as-judge, golden sets, regression tests, and before/after reports.
Model optimization
Compare quality, latency, and cost across model options before changing production routing.
Fine-tuning loops
Prepare data, run training configs, debug failed jobs, compare checkpoints, and decide if the fine-tune helped.
Prompt and grounding behavior
Find brittle instructions, hallucination triggers, refusal issues, tone regressions, and missing knowledge-gap handling.
NEO's Angle
It optimizes the loop, not a single output.
The RAG case study is the model: NEO measured quality first, found that retrieval was failing before generation, applied targeted fixes, then swept models only after the agent behavior improved.
Read the system before changing it
NEO inspects the repo, prompts, model calls, data loaders, eval files, notebooks, configs, and recent failures so the diagnosis starts from the real implementation.
Build a baseline
It defines quality, cost, latency, retrieval hit rate, pass/fail tests, or session-level metrics. The vague complaint becomes a number.
Find the layer that broke
The visible symptom may be a weak answer. The cause could be retrieval, context packing, routing, dataset noise, prompt drift, or an integration bug.
Change one thing at a time
NEO applies focused fixes, reruns the same benchmark, and compares the tradeoffs before recommending the next move.
Leave evidence behind
The output is a traceable report: what improved, what regressed, what changed in code, and what still needs a human decision.
Current Pipeline Debugging
Use it when the pipeline technically runs, but nobody trusts it.
Bring NEO to the system you already have. It does not need a greenfield demo to be useful.
A RAG assistant answers confidently but cites irrelevant context.
A fine-tune completes but fails on the task it was supposed to improve.
A model change lowers cost but quietly breaks refusal or tone behavior.
An agent works in demos but fails on real user inputs and tool latency.
Prompt changes help one workflow while regressing another.
Evaluation depends on spot checks that no one can reproduce.
Proof
A real RAG audit, turned into an eval loop.
NEO replaced gut-feel tuning with a repeatable benchmark, found a zero-context retrieval failure, added targeted fixes, and compared model options against the same six-turn session.
Read the full case study+19%quality after RAG fixes
-79%session cost after model sweep
7.88final judged quality score
The lasting win was the harness: future prompt, retrieval, and model changes can be compared against the same evidence instead of another round of manual spot checks.
Give NEO an AI system worth improving.
Start with a RAG app, model workflow, eval suite, or production agent that needs measurable improvement.