Overview

Afterimage is a powerful Python framework designed for generating high-quality synthetic conversation datasets using Large Language Models (LLMs). It provides a structured, scalable, and customizable way to simulate interactions between a user (Correspondent) and an AI assistant (Respondent), enabling the creation of diverse training and evaluation datasets for various applications.

Unlike simple prompt loops, Afterimage offers a sophisticated engine that manages personas, context retrieval (RAG), structured data extraction, and comprehensive quality evaluation, all while running asynchronously for maximum performance.

Use Cases

Afterimage is built to support a wide range of synthetic data needs:

  • Domain-Specific QA: Generate question-answer pairs based on your technical documentation to fine-tune models for expert-level support.

  • Enterprise Knowledge Training: Create safe, synthetic datasets from internal corporate wikis or knowledge bases to train models without exposing raw sensitive data.

  • Customer Support Simulation: Simulate realistic customer interaction scenarios—including angry, confused, or happy customers—to train support bots or human agents.

  • Data Extraction & Structuring: Convert unstructured text into strictly formatted JSON data for downstream processing or database population.

  • Evaluation Datasets: Produce “Golden Sets” of complex questions and ground-truth answers to benchmark the performance of your RAG pipelines.

Key Features

  • ⚡ Async & Parallel: Built on asyncio to generate hundreds of conversations concurrently, maximizing API throughput.

  • 🎭 Persona Simulation: Automatically generate unique user personas (e.g., “Novice User”, “Senior Engineer”) to ensure dataset diversity.

  • 🧠 RAG-Native: First-class support for injecting context from vector databases (like Qdrant) or local files into the generation process.

  • 📋 Structured Generation: Enforce Pydantic schemas on outputs to ensure generated data is machine-readable and valid.

  • ⚖️ Evaluation Framework: Built-in evaluators (both LLM-as-judge and embedding-based) to measure coherence, factuality, and grounding.

  • 📊 Monitoring: Real-time observability into generation metrics like token usage, meaningfulness, and error rates.

Key Concepts

To effectively use Afterimage, it helps to understand its core abstractions:

1. Correspondent vs. Respondent

  • Correspondent (The User): The simulated agent that initiates the conversation. It asks questions, follows up, or gives instructions. Its behavior is driven by an Instruction Generator.

  • Respondent (The Assistant): The agent that replies. Its behavior is defined by your system prompt. This is usually the model you are trying to emulate or train.

2. Generators

The engine that runs the simulation.

  • ConversationGenerator: The primary engine for multi-turn chat.

  • StructuredGenerator: Specialized engine for single-turn data extraction into JSON.

3. Callbacks

Hooks to customize the behavior of the agents:

  • Instruction Generator Callback: Decides what the Correspondent asks. It can read from documents to ask relevant questions.

  • Respondent Prompt Modifier: Dynamically changes the Respondent’s system prompt (e.g., injecting the relevant context/chunks for RAG).

4. Document Providers

The source of knowledge for the generation. Afterimage supports reading from:

  • Local text/markdown files.

  • JSONL datasets.

  • Vector Databases (Qdrant).

  • In-memory lists.

5. Personas

Profiles that shape the Correspondent’s tone, vocabulary, and expertise level. Instead of a generic “User”, Afterimage can simulate specific demographics to stress-test your model.