microsoft/promptbase
Promptbase
Promptbase is a prompt engineering framework designed for designing, testing, and optimizing prompts for large language models. It provides a system for measuring model accuracy and performance through an evaluation toolkit that compares outputs against ground-truth datasets. The project also includes an orchestration pipeline for automating multi-component machine learning tasks across cloud-based endpoints and a utility for preparing retrieval-augmented generation datasets.
The framework distinguishes itself through advanced response quality optimization, utilizing chain-of-thought generators to produce intermediate reasoning steps and dynamic few-shot example retrieval using embedding-based semantic search. It implements ensemble methods to increase predictive accuracy, employing complexity-based query routing and majority-vote aggregation of multiple model variations.
The system covers broader capabilities in data management and automation, including the formatting of external data into structured files for training and the orchestration of model execution pipelines via command-line utilities.
Features
- Prompt Engineering Frameworks - Offers a comprehensive system for designing, testing, and optimizing prompts using structured evaluation pipelines.
- Prompt Evaluation Tools - Measures the accuracy and performance of AI model outputs by comparing them against ground truth datasets.
- AI Model Benchmarking - Provides frameworks for running standardized tests to assess the performance and reliability of various prompting strategies.
- Automated Chain-of-Thought - Automatically generates step-by-step reasoning chains for training data by instructing models to think logically.
- Chain-of-Thought Prompting - Produces intermediate logic for training examples by prompting models to explain their own thought processes.
- Few-Shot Optimizers - Selects semantically similar training samples using embedding space clustering to provide dynamic context for model inputs.
- Answer Accuracy Evaluators - Implements automated metrics to score the semantic alignment between model responses and ground-truth reference answers.
- LLM Workflow Orchestrations - Automates the flow of data through cloud environments to orchestrate complex machine learning workflows and model calls.
- Machine Learning Pipelines - Automates the execution of multi-component machine learning tasks across cloud-based AI endpoints.
- Multi-Stage Pipeline Orchestrators - Provides a CLI tool for automating cloud environment setup and program uploads to execute multi-component ML workflows.
- Machine Learning Pipelines - Automates the deployment and execution of datasets through cloud endpoints via structured machine learning workflows.
- LLM Evaluation - Provides a toolkit for measuring model accuracy and performance by comparing outputs against ground-truth datasets.
- Response Quality Optimization - Employs dynamic few-shot selection and chain-of-thought strategies to elicit more accurate answers from foundation models.
- Prompt Strategy Routing - Analyzes query complexity to dynamically select the most effective prompting technique or reasoning path for a given input.
- RAG Dataset Formatters - Fetches external data and formats it into JSONL files for training and retrieval-augmented generation.
- Majority-Vote Ensembles - Increases predictive accuracy by combining outputs from multiple model variations using a majority-vote aggregation method.
- Ensemble Aggregations - Provides strategies for combining predictions from multiple model calls or prompt variations into a single output via majority voting.
- Dynamic Routing Strategies - Implements logic to evaluate request complexity and dynamically weight different prompting strategies within an ensemble.
- Technique Selection Systems - Selects specific prompting techniques based on query complexity to maintain high performance across diverse topics.
- Training Dataset Preparation - Formats external data into structured files and generates synthetic reasoning steps for model training.
- Semantic Example Retrieval - Finds semantically similar training samples in vector space to provide relevant context for few-shot prompting.
- Ensemble Prompt Routing - Aggregates results from multiple prompt variations and routes queries based on complexity to increase predictive accuracy.
