Edd

EDD — Eval-Driven Development

TDD is for code. EDD is for context.

Write behavioral assertions about the agent. Engineer context until they pass. Never ship a harness change without evidence it helped.

The Boundary Rule

Will this context/prompt run >~20 times?  →  Use EDD.
Still figuring out what "good" means?     →  Don't yet. Explore first.

EDD applies to harness artifacts — system prompts, tool definitions, retrieval strategies, instruction structures, few-shot examples. NOT for feature code (use TDD) or one-off prompts (use your eyes).

When It Shines

• Reusable harness iteration — every change validated, regressions caught
• "It used to work" — diff scores against last known-good
• Competing approaches — "few-shot vs. detailed instructions?" Run both
• Handoffs — assertions are executable documentation
• Safety/compliance — "never expose PII" is a natural assertion
• Diminishing returns — score curve tells you when to stop

When It Doesn't

• One-off tasks — assertion cost > output reading
• Exploratory phase — don't know what good looks like
• Creative/subjective output — taste, not measurable
• Rapid prototyping — eval friction kills exploration
• Simple mechanical prompts — eyeball in 2 seconds

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The EDD Loop

digraph edd {
    rankdir=TB;
    node [shape=box];

    define [label="1. DEFINE\nWrite 5-15 assertions\nacross categories"];
    cases [label="2. TEST CASES\n3-5 representative inputs"];
    baseline [label="3. BASELINE\nRun current harness\nagainst assertions\n(3-5 runs per case)"];
    change [label="4. CHANGE ONE THING\nModify one context variable"];
    eval [label="5. EVAL\nRun against assertions"];
    regressed [label="Regressed?" shape=diamond];
    improved [label="Improved?" shape=diamond];
    allpass [label="All passing?" shape=diamond];
    simplify [label="6. SIMPLIFY\nRemove non-load-bearing\ncontext (assertions\nstill pass without it?)"];
    suite [label="7. ADD TO SUITE\nAssertions join\nregression suite"];
    revert [label="Revert change\ntry different approach"];
    fix [label="Fix regression\nbefore continuing"];

    define -> cases -> baseline -> change -> eval -> regressed;
    regressed -> fix [label="yes"];
    fix -> eval;
    regressed -> improved [label="no"];
    improved -> change [label="no — revert & retry"];
    improved -> allpass [label="yes"];
    allpass -> change [label="no"];
    allpass -> simplify [label="yes"];
    simplify -> suite;
}

Key Discipline

• Step 4 is "change ONE thing" — not three. Otherwise you can't attribute improvement.
• Step 5 checks regressions first — improving 3 / breaking 2 is not progress.
• Step 6 prunes — if assertions pass without context X, X was deadwood.
• Multiple runs per eval — LLM output is stochastic. One run proves nothing.

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Assertion Taxonomy

Bad assertions give false confidence. Good assertions catch real regressions.

Behavioral — What the agent DOES

"Agent calls search_codebase before generating code"
"Agent asks for clarification when the task is ambiguous"
"Agent creates a test file before the implementation file"

Safety — What the agent must NOT do

"Agent never exposes API keys in output"
"Agent refuses to modify production database without confirmation"
"Agent does not hallucinate tool names that don't exist"

Structural — What the output LOOKS like

"Response includes a code block with the solution"
"Output follows the project's naming conventions"
"Generated files are placed in the correct directory"

Quality — How GOOD the output is

"Generated code handles the edge case described in the harness"
"Agent uses internal terminology, not generic alternatives"
"Solution addresses the root cause, not just the symptom"

Efficiency — How much WASTE is avoided

"Agent doesn't ask questions already answered in the harness"
"Agent takes fewer tool calls than baseline to reach same outcome"
"Agent doesn't generate-then-discard incorrect approaches"

Writing Good Assertions

Do	Don't
Specific and verifiable	Vague ("agent should be helpful")
Observable from output	Requires reading agent's mind
Discriminating (fails without harness)	Passes regardless of context
Independent (one thing per assertion)	Compound ("does X AND Y AND Z")

Litmus test: Reviewer grades PASS/FAIL in <30s by reading output? If not, sharpen.

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Confidence and Stochasticity

LLM outputs are non-deterministic. One run = anecdote.

How Many Runs?

Context	Min runs
Quick iteration	3
Confident change (shipping)	5
High-stakes (safety/compliance)	10+

What Counts as "Passing"?

Assertion passes when ≥80% of runs pass (4/5, 8/10). Adjust for stakes:

• Convenience harness: 70% acceptable
• Safety: 100% or fail

Detecting Flaky Assertions

Flaky = passes 40-60% consistently. Either:

• Poorly written — ambiguous grading. Fix the assertion.
• At capability boundary — context helps sometimes. Accept or redesign.

Diagnose, don't ignore.

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Grading

Deterministic (Preferred)

• String/regex matching
• Tool call sequence verification
• File existence / content checks
• Structured output validation

LLM-as-Judge

When assertions need judgment ("uses appropriate terminology", "handles edge case correctly"):

• Separate LLM call with assertion + output
• Provide explicit grading criteria, not just assertion text
• LLM judges are lenient by default. Add: "be skeptical — surface-level compliance is a FAIL"

Human Review

For high-stakes or distrust of automation:

• Output + assertion to developer
• Binary PASS/FAIL, no partial credit
• Batch reviews

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Integration with context-eval

EDD = methodology. context-eval = measurement engine.

Concern	EDD	context-eval
When to write assertions	Yes — before any change	No — takes as input
How to structure eval loop	Yes — change one, check regressions	No — single comparison
How to measure delta	No — delegates	Yes — pass rates, benefit-per-kilotoken
How to grade outputs	No — delegates	Yes — grading protocol
When to stop iterating	Yes — diminishing returns	No — reports, doesn't advise

EDD defines what to measure and when. context-eval measures. EDD interprets and decides next steps.

To run an eval cycle, use context-eval's loop (steps 2-7) with EDD's assertions and test cases. EDD adds the outer loop: which variable to change, regression checking, simplification pass.

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The Simplification Pass

Once green, remove context to verify what's load-bearing.

For each section/instruction in the harness:
  1. Remove temporarily
  2. Run eval suite
  3. Did any assertion regress?
     Yes → load-bearing. Keep.
     No  → deadwood. Cut permanently.

Highest-leverage step in the loop. Most harnesses carry 20-40% deadwood — tokens that don't change behavior. Cutting improves latency, cost, often output quality (less noise).

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Managing the Eval Suite Over Time

When to Add

• New capability → new assertions
• Bug in production → regression assertion before fix
• User reports unexpected behavior → encode as assertion

When to Retire

• Harness no longer claims that behavior
• Hasn't failed in 10+ cycles (consider promoting to spot-check)
• Non-discriminating (passes with and without harness)

Suite Hygiene

• Review full suite every ~10 iterations
• Fix flaky assertions — they erode trust
• Keep runnable in <5 min for iteration; separate "full suite" for pre-ship

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Anti-Patterns

Anti-pattern	Symptom	Fix
Vibes-driven iteration	"It seems better" without evidence	Run the eval.
Changing multiple variables	Can't attribute improvement	One change per cycle.
Assertion-free shipping	Changes ship without eval	No commit without green run.
Testing theater	Assertions always pass	Check discrimination — fails without harness?
Over-specifying	Assertions break on valid variations	Assert behavior, not exact wording.
Ignoring regressions	"That assertion wasn't important"	All regressions block until explicitly retired.
Skipping simplification	Harness grows monotonically	Prune after every green cycle.
Eval suite rot	Suite stale for months	Review every ~10 iterations.

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Quick Reference

EDD in 30 seconds:

1. Write assertions
2. Baseline
3. Change ONE thing
4. Eval (improved? regressions?)
5. Repeat 3-4 until all pass
6. Simplify (remove non-load-bearing)
7. Add to regression suite