We have GPT-2 running locally. Now let us build a chat interface around it – turning a raw language model into an interactive assistant. This is exactly what products like ChatGPT, Claude, and Gemini do, just at a much smaller scale.
Chat Harness: System Prompts and Conversation History
Specifically, a chat harness maintains a conversation history and prepends a system prompt that sets the assistant’s behavior. For example: “You are a helpful assistant. Answer concisely and accurately.” Every new user message is appended to the history, and the entire context is fed to the model for each response. The model generates a completion, which we extract and display.
Chat Harness Token Management
GPT-2 has a context window of 1024 tokens. Long conversations will exceed this limit, so we need a strategy: either truncate old messages, summarize the conversation, or use a sliding window. Modern chat systems like those built on OpenAI’s API handle this automatically with their chat completions endpoint.
Run chat.py to start a conversation. The harness formats messages with special tokens to distinguish user and assistant turns. While GPT-2 is far less capable than GPT-4, the architecture is identical – the difference is scale, data, and training methodology.
In the next lesson, we will enhance our chat system with retrieval-augmented generation (RAG), allowing it to answer questions about custom documents.
Chat Harness Streaming Responses
Users expect to see text appear incrementally, not wait for the full response. In practice, streaming generates tokens one at a time and sends each to the client as it is produced. In Python, this is done with a generator function that yields tokens as the model produces them. For web interfaces, Server-Sent Events (SSE) or WebSockets deliver the stream to the browser. The Gradio library makes this easy with its stream=True parameter, and Streamlit provides similar functionality for rapid prototyping of AI apps.
Chat Harness Multi-Turn Conversation Patterns
Additionally, a robust chat harness handles edge cases: empty responses (the model fails to generate), excessively long responses (cap with max_tokens), offensive content (content filter), and rate limiting. Modern frameworks like LangChain provide built-in support for conversation memory, message history, and prompt templates, abstracting away these details.
Chat Harness Prompt Templates and Few-Shot Examples
A chat harness can include few-shot examples in the system prompt to steer the model’s behavior. For a customer support bot, you might include example exchanges showing how to handle refunds, technical issues, and account problems. These examples teach the model the expected tone, format, and knowledge domain. The system prompt itself can be multiple paragraphs describing the assistant’s personality, constraints (“never make up information”), and format preferences (“use bullet points for lists”). LangChain’s PromptTemplate system makes it easy to manage, version, and test these prompts.
For production systems, however, consider using a dedicated inference engine like vLLM which provides optimized serving with continuous batching and PagedAttention, dramatically improving throughput for chat applications serving multiple users.
A well-designed chat harness also includes error handling: what happens when the model times out, generates an empty response, or produces toxic content? Graceful degradation with user-friendly error messages, retry logic, and content filtering are essential for production deployments. Start simple and iterate based on real user feedback.
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