LLM Workflow Diagnostics

Diagnose your AI workflow before upgrading your model.

Reduce token waste. Improve output quality. Diagnose the workflow before upgrading the model.

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full assessments completed

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avg overall workflow score

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quick checks run

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audited with AI evidence

// sample data — be the first to contribute

The Problem

Stop paying for bigger models to fix workflow problems.

The waste

Sessions bloat with re-explained context. Token spend climbs while output quality drops.

The cause

Workflow gaps — no briefing, no context hygiene, no session discipline — force the model to rediscover context every turn.

The fix

Diagnose where your workflow leaks tokens, then close those gaps before paying for a bigger model.

// the diagnostic →

The fix isn't a bigger model — it's a tighter loop.

Self-score vs. Evidence-based

Where your gut and the data disagree is where improvement lives.

Self-scoreYour gut

Project & Context Setup

75%

AI-audit−33pp gap

Project & Context Setup

42%

The Framework

Seven dimensions of LLM workflow quality.

Each one is a place where unclear context, weak prompts, or poor session hygiene burns tokens and degrades output.

Token simulator — toggle practices below

BaselineWith your practices

Tight project briefingReference vs. artifact separationSkill-as-needed loadingSession resets at drift

Total: ~4,368 tokens

Illustrative — directional research, not benchmarked

$ per 1k tokensWh per 1k tokens$ per kWh

Tokens saved / mo

API cost saved / mo

$0.00

Energy saved / mo

0.0 Wh · $0.000

Projected over ~200 sessions/mo. Energy estimate per public LLM inference research; actual values vary by model, hardware, and provider.

Project Setup

Whether you brief the model once with goals, constraints, and standards — or re-explain them every session.

Knowledge Quality

Whether the reference material you feed the model is current, structured, and trusted — or noisy and contradictory.

On-Demand Context

Whether you surface the right context at the right moment — or dump everything and hope the model finds it.

Prompt Quality

Whether prompts are specific, scoped, and verifiable — or vague asks that trigger expensive guessing.

Session Discipline

Whether you reset, compact, and scope sessions deliberately — or let context rot inflate every turn.

Efficiency

Whether you reach the answer in the fewest necessary turns — or burn tokens on rework and clarification loops.

Output Discernment

Whether you verify and act on outputs critically — or accept plausible-looking results that quietly drift off-spec.

Diagnose your workflow in 5 minutes.