Measure and Improve Your AI

Guide the user through measuring how well their AI works, then systematically improving it. This is a loop: define "good" -> measure -> improve -> verify.

The Workflow

•Define what "good" means — write a metric
•Measure current quality — run an evaluation
•Improve — choose an optimizer, run it
•Verify — re-evaluate to confirm improvement
•Iterate or ship

Step 1: Define what "good" means (write a metric)

A metric takes an expected answer and the AI's answer, and returns a score.

Exact match (simplest)

python

def metric(example, prediction, trace=None):
    return prediction.answer == example.answer

Normalized match (handles capitalization/whitespace)

python

def metric(example, prediction, trace=None):
    return prediction.answer.strip().lower() == example.answer.strip().lower()

Partial credit (for multi-field outputs)

python

def metric(example, prediction, trace=None):
    fields = ["name", "email", "phone"]
    correct = sum(
        1 for f in fields
        if getattr(prediction, f, "").lower() == getattr(example, f, "").lower()
    )
    return correct / len(fields)

F1 score (for text overlap)

python

def metric(example, prediction, trace=None):
    gold_tokens = set(example.answer.lower().split())
    pred_tokens = set(prediction.answer.lower().split())
    if not gold_tokens or not pred_tokens:
        return float(gold_tokens == pred_tokens)
    precision = len(gold_tokens & pred_tokens) / len(pred_tokens)
    recall = len(gold_tokens & pred_tokens) / len(gold_tokens)
    if precision + recall == 0:
        return 0.0
    return 2 * (precision * recall) / (precision + recall)

AI-as-judge (for open-ended tasks)

When exact match is too strict (summaries, creative tasks, open-ended Q&A):

python

class AssessQuality(dspy.Signature):
    """Assess if the predicted answer is correct and complete."""
    question: str = dspy.InputField()
    gold_answer: str = dspy.InputField()
    predicted_answer: str = dspy.InputField()
    is_correct: bool = dspy.OutputField()

def metric(example, prediction, trace=None):
    judge = dspy.Predict(AssessQuality)
    result = judge(
        question=example.question,
        gold_answer=example.answer,
        predicted_answer=prediction.answer,
    )
    return result.is_correct

Composite metric (multiple criteria)

python

def metric(example, prediction, trace=None):
    correct = float(prediction.answer.lower() == example.answer.lower())
    concise = float(len(prediction.answer.split()) < 50)
    has_reasoning = float(len(getattr(prediction, 'reasoning', '')) > 20)
    return 0.7 * correct + 0.2 * concise + 0.1 * has_reasoning

Training-aware metric

The trace parameter is not None during optimization. Use it for stricter requirements during training:

python

def metric(example, prediction, trace=None):
    correct = prediction.answer == example.answer
    if trace is not None:
        # During optimization, also require good reasoning
        has_reasoning = len(prediction.reasoning) > 50
        return correct and has_reasoning
    return correct

Step 2: Measure current quality (run evaluation)

Prepare test data

If you don't have enough examples, use /ai-generating-data to generate synthetic training data.

python

import dspy

# Manual creation
devset = [
    dspy.Example(question="What is DSPy?", answer="A framework for LM programs").with_inputs("question"),
    # 20-100+ examples for reliable evaluation
]

# From CSV/JSON
import json
with open("test_data.json") as f:
    data = json.load(f)
devset = [dspy.Example(**x).with_inputs("question") for x in data]

# From HuggingFace
from datasets import load_dataset
dataset = load_dataset("squad", split="validation[:100]")
devset = [
    dspy.Example(question=x["question"], answer=x["answers"]["text"][0]).with_inputs("question")
    for x in dataset
]

Run evaluation

python

from dspy.evaluate import Evaluate

evaluator = Evaluate(
    devset=devset,
    metric=metric,
    num_threads=4,
    display_progress=True,
    display_table=5,   # show 5 example results
)

baseline_score = evaluator(my_program)
print(f"Baseline: {baseline_score}")

Step 3: Improve (choose an optimizer)

Quick guide: which optimizer?

Training examples	Recommended optimizer	Expected improvement	Typical cost
<20	GEPA (instruction tuning)	5-15%	~$0.50
20-50	BootstrapFewShot	5-20%	~$0.50-2
50-200	BootstrapFewShot, then MIPROv2	15-35%	~$2-10
200-500	MIPROv2 (auto="medium")	20-40%	~$5-15
500+	MIPROv2 (auto="heavy") or BootstrapFinetune	25-50%	~$15-50+

code

Start here
|
+- Just getting started (<50 examples)? -> BootstrapFewShot
|   Quick, cheap, usually gives a solid boost.
|
+- Want better prompts (50+ examples)? -> MIPROv2
|   Optimizes both instructions and examples.
|   Best general-purpose prompt optimizer.
|
+- Want to tune instructions only (<50 examples)? -> GEPA
|   Good when you have few examples.
|
+- Need maximum quality (500+ examples)? -> BootstrapFinetune
|   Fine-tunes the model weights.
|   Best for production with smaller/cheaper models.
|
+- Want to combine approaches? -> BetterTogether
    Jointly optimizes prompts and weights.

Stacking tip: Run BootstrapFewShot first, then MIPROv2 on the result. This often beats either alone — bootstrap finds good examples, then MIPRO refines the instructions.

Optimized prompts are model-specific. If you change models, re-run your optimizer. See /ai-switching-models.

BootstrapFewShot (start here)

Fast, cheap. Finds good examples by running your program and keeping successful traces.

python

optimizer = dspy.BootstrapFewShot(
    metric=metric,
    max_bootstrapped_demos=4,
    max_labeled_demos=4,
)
optimized = optimizer.compile(my_program, trainset=trainset)

Cost: Minimal (one pass through trainset). Expected improvement: 5-20%.

MIPROv2 (recommended for most cases)

Optimizes both instructions and examples. Best general-purpose optimizer.

python

optimizer = dspy.MIPROv2(
    metric=metric,
    auto="medium",    # "light", "medium", "heavy"
)
optimized = optimizer.compile(my_program, trainset=trainset)

•"light": Quick, ~$1-2
•"medium": Balanced, ~$5-10
•"heavy": Thorough, ~$15-30

Expected improvement: 15-35%.

GEPA (instruction tuning)

Good with few examples or when you want to focus on instruction quality:

python

optimizer = dspy.GEPA()
optimized = optimizer.compile(my_program, trainset=trainset, metric=metric)

BootstrapFinetune (maximum quality)

Fine-tunes model weights for the biggest accuracy gains. Requires 500+ examples and a fine-tunable model:

python

optimizer = dspy.BootstrapFinetune(metric=metric, num_threads=24)
optimized = optimizer.compile(my_program, trainset=trainset)

For the full fine-tuning workflow (decision framework, prerequisites, model distillation, BetterTogether), see /ai-fine-tuning.

When optimization plateaus

If your score stops improving, check these common causes:

Symptom	Likely cause	Fix
Score stuck at 60-70% despite optimization	Task too complex for single step	`/ai-decomposing-tasks` — break into subtasks
Optimizer overfits (train score high, dev score flat)	Too little training data	`/ai-generating-data` — generate more examples
Score varies wildly between runs	Non-deterministic metric or small devset	Increase devset to 100+, set temperature=0
Diminishing returns from more demos	Prompt is maxed out; model is the limit	`/ai-switching-models` — try a more capable model
Score high but real users complain	Metric doesn't match real quality	Rewrite metric based on actual failure patterns

Step 4: Verify improvement

python

optimized_score = evaluator(optimized)
print(f"Baseline: {baseline_score:.1f}%")
print(f"Optimized: {optimized_score:.1f}%")
print(f"Improvement: {optimized_score - baseline_score:.1f}%")

Step 5: Save and ship

python

optimized.save("optimized_program.json")

# Load later
my_program = MyProgram()
my_program.load("optimized_program.json")

Key patterns

•Start simple: exact match metric + BootstrapFewShot, then upgrade if needed
•Validate your metric: manually check 10-20 examples to make sure the metric scores correctly
•More data helps: optimizers work better with more training examples
•Never evaluate on trainset: always use a held-out devset
•Use display_table: looking at actual predictions reveals metric bugs
•Iterate: run optimization, check results, improve metric, re-optimize

Additional resources

•For optimizer comparison table and metric patterns, see reference.md
•Once quality is good, use /ai-cutting-costs to reduce your AI bill
•Use /ai-monitoring to track quality in production after deployment
•Use /ai-tracking-experiments to log, compare, and manage multiple optimization runs
•Accuracy plateaued despite optimization? Try /ai-decomposing-tasks to restructure your task
•If things are broken, use /ai-fixing-errors to diagnose