Camel + LangChain for Synthetic Data & Market Research

Camel—an “autonomous GPT” approach built around two agents talking to each other—gets positioned as a practical engine for synthetic data and market research. Instead of a single assistant responding to a single user, Camel orchestrates a back-and-forth conversation where roles (and their prompts) drive the interaction. That structure matters because it can generate large volumes of realistic dialogue that can later be used to train or fine-tune customer-service bots, chat agents, and other models that depend on human-like conversational behavior.

The discussion ties Camel to a broader trend: using large language models such as ChatGPT and GPT-4 as stand-ins for real consumers. In market research, people prompt the model to behave like a specific type of customer, then probe preferences, reactions, and messaging effectiveness. Reported results suggest the model’s responses often track what real humans say, which makes synthetic “consumer” conversations useful for exploring hypotheses before spending time or money on human studies. A similar idea is described for political polling—having the model role-play as a voter in a region with certain issues to test which arguments or messages feel persuasive.

Camel’s core mechanics are explained through two prompting techniques. First is role-playing: agents are assigned distinct personas (for example, a local resident critiquing an itinerary), and the conversation produces more grounded feedback—such as local tips on where to eat or what to see—because the model is steered to speak from that perspective. Second is “inception prompting,” where a prompt generates another, more detailed prompt. The example given starts with a rough request (“help me plan a trip to Singapore”), then uses follow-up questions (duration, activities, budget) to produce a specific multi-day itinerary prompt that the agents can execute.

In the paper’s workflow, a human supplies a simple task, inception prompting expands it into a richer task description, and additional prompts define how each agent should respond in its role. The system then runs multiple turns where one agent’s output becomes the other agent’s input, enabling cooperative completion of the task. The transcript also flags recurring failure modes in multi-agent chat: “role flipping” (agents swap roles midstream), assistant repetition of instructions, and low-quality replies that can spiral into infinite loops. The mitigation approach described is prompt tuning and iterative refinement to keep the conversation stable and terminating.

Concrete scenarios illustrate the payoff. The “AI society” dataset uses combinations of assistant roles, user roles, and domains; a coding co-generation setup pairs code languages with tasks; and the demos let users pick assistant/user roles and generate inception prompts automatically. Dataset scale is cited as roughly 50,000 examples for code chat and about 25,000 for AI society.

Finally, the walkthrough shifts to code: a modified LangChain implementation of Camel using OpenAI’s GPT-3.5-turbo. The implementation centers on an agent class that manages system/human/AI message formatting, stores conversation state, and runs a step function to get model responses. The example then demonstrates a market-research-style conversation between a “Singapore tourism board” representative and a first-time tourist, using inception prompting to expand the initial task and a loop (e.g., 15 turns per side) to generate and save the full dialogue. Token usage and cost are tracked at the end, with the example conversation reported as inexpensive—while also noting that many open-source models may struggle because they’re trained more for instruction following than chat-style interaction.