PromptXpert
Automated prompt-engineering toolkit built on DSPy that trains programs to optimize any prompt for clarity, reasoning, and task accuracy.
Overview
Handcrafting prompts is brittle, time-consuming, and subjective. PromptXpert treats prompt engineering as a mathematical optimization task. Built on the DSPy programming model, it automatically compiles and optimizes prompt instructions, few-shot exemplars, and reasoning chains against a validated metric and dataset.
Key Metrics & Performance
Architecture & Design
PromptXpert integrates DSPy optimizers (BootstrapFewShot, MIPROv2, and Bayesian Signature Optimization) with a custom teleprompter evaluation harness. Users provide input signatures, a metric function, and a minimal set of train examples; the optimizer explores candidate instructions and select high-utility exemplars via multi-stage feedback loops.
Key Capabilities
- Automated prompt synthesis using MIPROv2 & Bayesian prompt search
- Dataset evaluation against custom multi-metric loss functions
- Zero-code CLI for transforming brittle string templates into robust DSPy modules
- Comprehensive tracing and cost attribution across LLM providers
Usage & Integration
import dspy
from promptxpert.optimizers import BayesianInstructionOptimizer
class ReasoningSignature(dspy.Signature):
"""Solve complex software architecture dilemmas with step-by-step logic."""
context = dspy.InputField(desc="System requirements and constraints")
decision = dspy.OutputField(desc="Recommended architecture with justification")
optimizer = BayesianInstructionOptimizer(
metric=custom_accuracy_metric,
num_candidates=20
)
compiled_program = optimizer.compile(ReasoningSignature, trainset=train_data)