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Clarity

Philip Stevens

Vancouver

AI systems often behave unpredictably once real users touch them. I help teams diagnose failure modes, stabilize outputs, improve retrieval grounding, choose the right adaptation strategy and prevent silent regressions.
Former Agoda and Quantcast. Ten years building and optimizing production ML systems at scale.
Clients call when reliability, accuracy or cost becomes a blocker and they need expert triage fast.

$2.50/min

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Data Science Machine Learning Artificial Intelligence

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Emergency LLM Failure Mode Triage (Root Cause)

When something is failing and you cannot tell why, I help you pinpoint the real cause fast.

If you are seeing hallucinations, drift, unstable formatting, retrieval issues, latency spikes or unexpected regressions, this session identifies what is actually breaking and what to fix first.

You will get:
- A clear diagnosis of the real failure source
- A shortlist of fixes with the highest immediate impact
- Reduced risk for customer or investor facing interactions
- Guidance on whether to prompt, adapt, fine-tune or retrain
- Situations where you should avoid fine-tuning entirely

Bring failing examples or outputs.
This call reveals the correct path forward.

Model Adaptation Strategy: Fine-Tuning, Compression, Distillation

Choosing the wrong adaptation path wastes time and budget. I help you select the right approach for your use case without overspecializing models that later break in production.

You will get:
- Architecture recommendations
- Dataset quality checks that prevent overfitting
- Warning signs for catastrophic forgetting
- Clear trade-offs across cost, latency and accuracy
- When synthetic data genuinely helps
- When to combine compression with tuning

Bring sample training data if possible.
Avoiding one unnecessary adaptation often saves thousands.

Retrieval Grounding and Hallucination Debugging (RAG)

Most hallucinations come from retrieval weaknesses, not reasoning issues. I help you strengthen retrieval so output quality improves immediately.

You will get:
- RAG architecture guidance
- Chunking and embedding patterns that preserve meaning
- Retrieval tuning for phrasing and query variance
- Grounding strategies that work without retraining
- Considerations for memory layers such as scratchpads or caches

Bring failing queries.
Better retrieval coherence can sharply reduce hallucinations.

Reliability Harness, Regression Detection and Cost Control

I help teams build lightweight evaluation loops that catch behavioral regressions, formatting breaks and silent drift before they hit production.

You will get:
- Behavioral test design
- JSON, XML and schema-safe output constraints
- Drift alerts for silent behavior changes
- Lightweight scoring and evaluation patterns
- Interpretability checks that are simple and practical
- Compression guardrails that reduce cost without quality loss

Ideal for teams scaling beyond prompt testing.
Catching one silent regression can save weeks of work.

AI Output Stability and Consistency for Customer-Facing Flows

I help you stabilize model behavior anywhere consistency matters.

Prompt tests often miss variance drivers that appear only under real usage. I surface those hidden patterns and show you how to control them.

You will walk away with:
- Methods to reduce hallucinations on live queries
- Patterns for predictable structured output
- Tone and brand consistency guidelines
- Simple fallback paths for high-stakes flows

Best for teams shipping customer-visible AI features, demos or investor materials.
Bring examples if you have them.

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Member since March 2025