Eval Driven Dev

Eval-Driven Development with pixie

This skill is about doing the work, not describing it. When a user asks you to set up evals for their app, you should be reading their code, editing their files, running commands, and producing a working test pipeline — not writing a plan for them to follow later.

The loop is: understand the app → instrument it → write the test file → build a dataset → run the tests → investigate failures → iterate. In practice the stages blur and you'll be going back and forth, but this ordering helps: write all the files (instrumentation, test file, MEMORY.md) before running any commands. That way your work survives even if an execution step hits a snag.

All pixie-generated files live in a single .pixie directory at the project root:

.pixie/
  MEMORY.md              # your understanding and eval plan
  observations.db        # SQLite trace DB (auto-created by enable_storage)
  datasets/              # golden datasets (JSON files)
  tests/                 # eval test files (test_*.py)
  scripts/               # helper scripts (build_dataset.py, etc.)

Stage 0: Ensure pixie-qa is Installed and API Keys Are Set

Before doing anything else, check that the pixie-qa package is available:

python -c "import pixie" 2>/dev/null && echo "installed" || echo "not installed"

If it's not installed, install it:

pip install pixie-qa

This provides the pixie Python module, the pixie CLI, and the pixie test runner — all required for instrumentation and evals. Don't skip this step; everything else in this skill depends on it.

Verify API keys

The application under test almost certainly needs an LLM provider API key (e.g. OPENAI_API_KEY, ANTHROPIC_API_KEY). LLM-as-judge evaluators like FactualityEval also need OPENAI_API_KEY. Before running anything, verify the key is set:

[ -n "$OPENAI_API_KEY" ] && echo "OPENAI_API_KEY set" || echo "OPENAI_API_KEY missing"

If not set, ask the user. Do not proceed with running the app or evals without it — you'll get silent failures or import-time errors.

Stage 1: Understand the Application

Before touching any code, spend time actually reading the source. The code will tell you more than asking the user would, and it puts you in a much better position to make good decisions about what and how to evaluate.

What to investigate

1. How the software runs: What is the entry point? How do you start it? Is it a CLI, a server, a library function? What are the required arguments, config files, or environment variables?

2. All inputs to the LLM: This is not limited to the user's message. Trace every piece of data that gets incorporated into any LLM prompt:

   • User input (queries, messages, uploaded files)
   • System prompts (hardcoded or templated)
   • Retrieved context (RAG chunks, search results, database records)
   • Tool definitions and function schemas
   • Conversation history / memory
   • Configuration or feature flags that change prompt behavior

3. All intermediate steps and outputs: Walk through the code path from input to final output and document each stage:

   • Retrieval / search results
   • Tool calls and their results
   • Agent routing / handoff decisions
   • Intermediate LLM calls (e.g., summarization before final answer)
   • Post-processing or formatting steps

4. The final output: What does the user see? What format is it in? What are the quality expectations?

5. Use cases and expected behaviors: What are the distinct things the app is supposed to handle? For each use case, what does a "good" response look like? What would constitute a failure?

Write MEMORY.md

Write your findings down in .pixie/MEMORY.md. This is the primary working document for the eval effort. It should be human-readable and detailed enough that someone unfamiliar with the project can understand the application and the eval strategy.

CRITICAL: MEMORY.md documents your understanding of the existing application code. It must NOT contain references to pixie commands, instrumentation code you plan to add, or scripts/functions that don't exist yet. Those belong in later sections, only after they've been implemented.

The understanding section should include:

# Eval Notes: <Project Name>

## How the application works

### Entry point and execution flow

<Describe how to start/run the app, what happens step by step>

### Inputs to LLM calls

<For each LLM call in the codebase, document:>

- Where it is in the code (file + function name)
- What system prompt it uses (quote it or summarize)
- What user/dynamic content feeds into it
- What tools/functions are available to it

### Intermediate processing

<Describe any steps between input and output:>
- Retrieval, routing, tool execution, etc.
- Include code pointers (file:line) for each step

### Final output

<What the user sees, what format, what the quality bar should be>

### Use cases

<List each distinct scenario the app handles, with examples of good/bad outputs>

## Evaluation plan

### What to evaluate and why

<Quality dimensions: factual accuracy, relevance, format compliance, safety, etc.>

### Evaluation granularity

<Which function/span boundary captures one "test case"? Why that boundary?>

### Evaluators and criteria

<For each eval test, specify: evaluator, dataset, threshold, reasoning>

### Data needed for evaluation

<What data points need to be captured, with code pointers to where they live>

If something is genuinely unclear from the code, ask the user — but most questions answer themselves once you've read the code carefully.

Stage 2: Decide What to Evaluate

Now that you understand the app, you can make thoughtful choices about what to measure:

• What quality dimension matters most? Factual accuracy for QA apps, output format for structured extraction, relevance for RAG, safety for user-facing text.
• Which span to evaluate: the whole pipeline (root) or just the LLM call (last_llm_call)? If you're debugging retrieval, you might evaluate at a different point than if you're checking final answer quality.
• Which evaluators fit: see references/pixie-api.md → Evaluators. For factual QA: FactualityEval. For structured output: ValidJSONEval / JSONDiffEval. For RAG pipelines: ContextRelevancyEval / FaithfulnessEval.
• Pass criteria: ScoreThreshold(threshold=0.7, pct=0.8) means 80% of cases must score ≥ 0.7. Think about what "good enough" looks like for this app.
• Expected outputs: FactualityEval needs them. Format evaluators usually don't.

Update .pixie/MEMORY.md with the plan before writing any code.

Stage 3: Instrument the Application

Add pixie instrumentation to the existing application code. The goal is to capture the inputs and outputs of existing functions as observable spans. Do not add new functions or change the application's behavior — only wrap existing code paths.

Add enable_storage() at application startup

Call enable_storage() once at the beginning of the application's startup code — inside main(), or at the top of a server's initialization. Never at module level (top of a file outside any function), because that causes storage setup to trigger on import.

Good places:

• Inside if __name__ == "__main__": blocks
• In a FastAPI lifespan or on_startup handler
• At the top of main() / run() functions
• Inside the runnable function in test files

# ✅ CORRECT — at application startup
async def main():
    enable_storage()
    ...

# ✅ CORRECT — in a runnable for tests
def runnable(eval_input):
    enable_storage()
    my_function(**eval_input)

# ❌ WRONG — at module level, runs on import
from pixie import enable_storage
enable_storage()  # this runs when any file imports this module!

Wrap existing functions with @observe

@observe on an existing function captures all its kwargs as eval_input and its return value as eval_output. Apply it to the existing function that represents one "test case" — typically the outermost function a user interaction flows through:

from pixie import observe

@observe(name="answer_question")
def answer_question(question: str, context: str) -> str:  # existing function
    ...  # existing code, unchanged

For more control, use the context manager around existing code:

from pixie import start_observation

def process_request(query: str) -> str:  # existing function
    with start_observation(input={"query": query}, name="process_request") as obs:
        result = existing_pipeline(query)  # existing code
        obs.set_output(result)
        obs.set_metadata("chunks_retrieved", len(chunks))
    return result

CRITICAL rules:

• Never add new wrapper functions to the application code. Wrap existing functions in-place.
• Never change the function's interface (arguments, return type, behavior).
• The instrumentation is purely additive — if you removed all pixie imports and decorators, the app would work identically.
• After instrumentation, call flush() at the end of runs to make sure all spans are written.

Important: All pixie symbols are importable from the top-level pixie package. Never tell users to import from submodules (pixie.instrumentation, pixie.evals, pixie.storage.evaluable, etc.) — always use from pixie import ....

Stage 4: Write the Eval Test File

Write the test file before building the dataset. This might seem backwards, but it forces you to decide what you're actually measuring before you start collecting data — otherwise the data collection has no direction.

Create .pixie/tests/test_<feature>.py. The pattern is: a runnable adapter that calls your app function, plus an async test function that calls assert_dataset_pass:

from pixie import enable_storage, assert_dataset_pass, FactualityEval, ScoreThreshold, last_llm_call

from myapp import answer_question


def runnable(eval_input):
    """Replays one dataset item through the app. enable_storage() here ensures traces are captured."""
    enable_storage()
    answer_question(**eval_input)


async def test_factuality():
    await assert_dataset_pass(
        runnable=runnable,
        dataset_name="<dataset-name>",
        evaluators=[FactualityEval()],
        pass_criteria=ScoreThreshold(threshold=0.7, pct=0.8),
        from_trace=last_llm_call,
    )

Note that enable_storage() belongs inside the runnable, not at module level in the test file — it needs to fire on each invocation so the trace is captured for that specific run.

The test runner is pixie test (not pytest):

pixie test                           # run all test_*.py in current directory
pixie test .pixie/tests/             # specify path
pixie test -k factuality             # filter by name
pixie test -v                        # verbose: shows per-case scores and reasoning

pixie test automatically adds the project root and parent directories to sys.path, so imports of your application modules work without any extra configuration.

Stage 5: Build the Dataset

Create the dataset first, then populate it by actually running the app with representative inputs. This is critical — dataset items should contain real app outputs and trace metadata, not fabricated data.

pixie dataset create <dataset-name>
pixie dataset list   # verify it exists

Run the app and capture traces to the dataset

Write a simple script (.pixie/scripts/build_dataset.py) that calls the instrumented function for each input, flushes traces, then saves them to the dataset:

import asyncio
from pixie import enable_storage, flush, DatasetStore, Evaluable

from myapp import answer_question

GOLDEN_CASES = [
    ("What is the capital of France?", "Paris"),
    ("What is the speed of light?", "299,792,458 meters per second"),
]

async def build_dataset():
    enable_storage()
    store = DatasetStore()
    try:
        store.create("qa-golden-set")
    except FileExistsError:
        pass

    for question, expected in GOLDEN_CASES:
        result = answer_question(question=question)
        flush()

        store.append("qa-golden-set", Evaluable(
            eval_input={"question": question},
            eval_output=result,
            expected_output=expected,
        ))

asyncio.run(build_dataset())

Alternatively, use the CLI for per-case capture:

# Run the app (enable_storage() must be active)
python -c "from myapp import main; main('What is the capital of France?')"

# Save the root span to the dataset
pixie dataset save <dataset-name>

# Or specifically save the last LLM call:
pixie dataset save <dataset-name> --select last_llm_call

# Add context:
pixie dataset save <dataset-name> --notes "basic geography question"

# Attach expected output for evaluators like FactualityEval:
echo '"Paris"' | pixie dataset save <dataset-name> --expected-output

Key rules for dataset building:

• Always run the app — never fabricate eval_output manually. The whole point is capturing what the app actually produces.
• Include expected outputs for comparison-based evaluators like FactualityEval.
• Cover the range of inputs you care about: normal cases, edge cases, things the app might plausibly get wrong.
• When using pixie dataset save, the evaluable's eval_metadata will automatically include trace_id and span_id for later debugging.

Stage 6: Run the Tests

pixie test .pixie/tests/ -v

The -v flag shows per-case scores and reasoning, which makes it much easier to see what's passing and what isn't. Check that the pass rates look reasonable given your ScoreThreshold.

Stage 7: Investigate Failures

When tests fail, the goal is to understand why, not to adjust thresholds until things pass. Investigation must be thorough and documented — the user needs to see the actual data, your reasoning, and your conclusion.

Step 1: Get the detailed test output

pixie test .pixie/tests/ -v    # shows score and reasoning per case

Capture the full verbose output. For each failing case, note:

• The eval_input (what was sent)
• The eval_output (what the app produced)
• The expected_output (what was expected, if applicable)
• The evaluator score and reasoning

Step 2: Inspect the trace data

For each failing case, look up the full trace to see what happened inside the app:

from pixie import DatasetStore

store = DatasetStore()
ds = store.get("<dataset-name>")
for i, item in enumerate(ds.items):
    print(i, item.eval_metadata)   # trace_id is here

Then inspect the full span tree:

import asyncio
from pixie import ObservationStore

async def inspect(trace_id: str):
    store = ObservationStore()
    roots = await store.get_trace(trace_id)
    for root in roots:
        print(root.to_text())   # full span tree: inputs, outputs, LLM messages

asyncio.run(inspect("the-trace-id-here"))

Step 3: Root-cause analysis

Walk through the trace and identify exactly where the failure originates. Common patterns:

Step 4: Document findings in MEMORY.md

Every failure investigation must be documented in .pixie/MEMORY.md in a structured format:

### Investigation: <test_name> failure — <date>

**Test**: `test_faq_factuality` in `.pixie/tests/test_customer_service.py`
**Result**: 3/5 cases passed (60%), threshold was 80% ≥ 0.7

#### Failing case 1: "What rows have extra legroom?"

- **eval_input**: `{"user_message": "What rows have extra legroom?"}`
- **eval_output**: "I'm sorry, I don't have the exact row numbers for extra legroom..."
- **expected_output**: "rows 5-8 Economy Plus with extra legroom"
- **Evaluator score**: 0.1 (FactualityEval)
- **Evaluator reasoning**: "The output claims not to know the answer while the reference clearly states rows 5-8..."

**Trace analysis**:
Inspected trace `abc123`. The span tree shows:

1. Triage Agent routed to FAQ Agent ✓
2. FAQ Agent called `faq_lookup_tool("What rows have extra legroom?")` ✓
3. `faq_lookup_tool` returned "I'm sorry, I don't know..." ← **root cause**

**Root cause**: `faq_lookup_tool` (customer_service.py:112) uses keyword matching.
The seat FAQ entry is triggered by keywords `["seat", "seats", "seating", "plane"]`.
The question "What rows have extra legroom?" contains none of these keywords, so it
falls through to the default "I don't know" response — even though the seat FAQ
entry contains exactly the information requested ("Rows 5-8 are Economy Plus, with extra legroom").

**Fix**: Add `"row"`, `"rows"`, `"legroom"` to the seating keyword list in
`faq_lookup_tool` (customer_service.py:130).

**Verification**: After fix, re-run:
\`\`\`bash
python .pixie/scripts/build_dataset.py # refresh dataset
pixie test .pixie/tests/ -k faq -v # verify
\`\`\`

Step 5: Fix and re-run

Make the targeted change, rebuild the dataset if needed, and re-run. Always finish by giving the user the exact commands to verify:

pixie test .pixie/tests/test_<feature>.py -v

Memory Template

# Eval Notes: <Project Name>

## How the application works

### Entry point and execution flow

<How to start/run the app. Step-by-step flow from input to output.>

### Inputs to LLM calls

<For EACH LLM call, document: location in code, system prompt, dynamic content, available tools>

### Intermediate processing

<Steps between input and output: retrieval, routing, tool calls, etc. Code pointers for each.>

### Final output

<What the user sees. Format. Quality expectations.>

### Use cases

<Each scenario with examples of good/bad outputs:>

1. <Use case 1>: <description>
   - Input example: ...
   - Good output: ...
   - Bad output: ...

## Evaluation plan

### What to evaluate and why

<Quality dimensions and rationale>

### Evaluators and criteria

| Test | Dataset | Evaluator | Criteria | Rationale |
| ---- | ------- | --------- | -------- | --------- |
| ...  | ...     | ...       | ...      | ...       |

### Data needed for evaluation

<What data to capture, with code pointers>

## Datasets

| Dataset | Items | Purpose |
| ------- | ----- | ------- |
| ...     | ...   | ...     |

## Investigation log

### <date> — <test_name> failure

<Full structured investigation as described in Stage 7>

Reference

See references/pixie-api.md for all CLI commands, evaluator signatures, and the Python dataset/store API.