> ## Documentation Index
> Fetch the complete documentation index at: https://docs.aevyra.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Analysis output

> Understanding the optimization report — trajectory, strategy breakdown, prompt diff, and before/after examples.

After optimization, reflex produces a detailed analysis that explains what
happened and teaches prompt engineering principles. This page walks through each
section.

## Score trajectory

```
Trajectory : 0.350 → 0.450 → 0.520 → 0.600 → 0.650 → 0.720 → 0.780 → 0.850 → 0.880
```

The trajectory shows every iteration's score. Reflex analyzes the shape:

* **Steady climb** — consistent improvement across iterations
* **Plateau** — scores flatten, suggesting diminishing returns from the current approach
* **Over-optimization** — scores peak then regress (model may be overfitting to a pattern)
* **Gap closed** — how much of the remaining gap (to 1.0) was closed

If the result didn't converge, reflex suggests next steps: trying a different
strategy, adding more data, or adjusting the threshold.

## Strategy breakdown

When using auto mode, reflex shows what each phase contributed:

```
Strategy breakdown:
  Phase 1 — structural    : 0.350 → 0.520 (+0.170)
  Phase 2 — iterative     : 0.520 → 0.780 (+0.260)
  Phase 3 — fewshot       : 0.780 → 0.880 (+0.100)
```

Each phase also includes an educational lesson explaining *why* that technique
helped (or didn't):

* **Structural helped** — "Structure matters: reorganizing how instructions are
  presented can dramatically improve model comprehension."
* **Iterative helped** — "Specificity matters: models follow precise, explicit
  instructions better than vague ones."
* **Fewshot helped** — "Examples matter: showing the model what good output
  looks like is one of the most reliable ways to improve quality."
* **Phase hurt performance** — the analysis explains what went wrong and what
  to avoid

## Prompt diff

```
What changed in the prompt:
  - Much longer (5 → 47 words). The model needed more detailed instructions.
  - Added: markdown headers for clear sections, bold emphasis on key
    instructions, XML tags for structural clarity, explicit constraints
    on what to avoid
```

Reflex compares the original and optimized prompts, highlighting:

* Length changes and what they mean
* New structural features (headers, bullets, XML tags, examples)
* Added constraints or format specifications

## Before / after example

```
BEFORE / AFTER EXAMPLE (most-improved sample):
  Input:  Summarize photosynthesis.
  BEFORE (score: 0.25):
    Plants use light.
  AFTER (score: 0.75):
    Plants convert sunlight into chemical energy using chlorophyll...
  Score change: 0.25 → 0.75 (+0.50)
```

Reflex picks the sample with the largest score improvement and shows the
concrete difference the optimized prompt made.

## Programmatic access

All analysis data is available programmatically:

```python theme={null}
result = optimizer.run("You are a helpful assistant.")

# Full formatted summary (includes all sections above)
print(result.summary())

# Raw data
result.score_trajectory       # [0.35, 0.45, ...]
result.improvement            # 0.58
result.improvement_pct        # 193.3
result.phase_history          # [{"phase": 1, "axis": "structural", ...}]

# Serialize
result.to_json("results.json")
result.save_best_prompt("best_prompt.md")
```