Harness for Smarter Prompting

You are an expert AI systems architect, prompt engineer, and product strategist. Your job is to design a practical harness that sits in front of an LLM and decides, for each request, how much context to send, how many tokens are likely to be used, what output length to target, and which model tier to use. The end user is not technical and has no mental model for prompting an LLM, so you must explain ideas using plain English and a simple simile. Use this kind of framing: **it is like popping to the shops in a V8 SUV when a hybrid city car will do the job**. The point is to show that some tasks need a big expensive machine, but many can be done faster, cheaper, and more efficiently with a smaller one. ## Your task Create a complete design for an AI harness that: - Estimates input and output token usage before a model is called. - Suggests the most appropriate model based on task complexity, cost, latency, and quality needs. - Rewrites or trims prompts to remove waste. - Applies simple routing rules for common task types. - Logs actual usage versus predicted usage. - Learns from past outcomes to improve future routing. - Presents the whole thing in a way a non-technical business user can understand. ## What to produce Return the answer in this structure: 1. **Plain-English explanation** - Explain what the harness does in simple terms. - Use the vehicle simile naturally. - Avoid jargon unless you immediately define it. 2. **System design** - Describe the main components. - Include prompt analyser, token estimator, model router, prompt refiner, cost calculator, and logging layer. - Explain the flow from user request to model selection to result. 3. **Decision rules** - Give practical routing rules. - For example: simple extraction tasks go to a smaller model; messy reasoning or ambiguous tasks go to a larger one. - Include a fallback rule when confidence is low. 4. **Prompt optimisation rules** - Explain how to shorten prompts without losing important context. - Show how to remove duplication, irrelevant detail, and vague instructions. - Explain how to preserve constraints, tone, and output format. 5. **Outputs and telemetry** - Specify what should be logged. - Include estimated tokens, actual tokens, model chosen, cost estimate, latency, and outcome quality. - Explain how the logs improve future decisions. 6. **Implementation sketch** - Provide a high-level architecture in pseudocode or structured steps. - Keep it accessible, not deeply technical. - Include optional API schema or data fields if useful. 7. **Example** - Show one example user request. - Show how the harness would classify it. - Show which model it would choose and why. - Show a shortened prompt version. 8. **Cautions** - Mention failure modes, such as over-trimming context, choosing too small a model, or relying too heavily on token counts alone. - Explain that quality checks still matter. ## Style requirements - Write for a business audience with no LLM prompting background. - Use short paragraphs and bullet points. - Prefer concrete examples over abstract theory. - Do not assume the reader knows what a token is; define it simply as a rough “piece” of text the model reads or writes. - Make the simile memorable and easy to understand. - Be decisive and practical. - If you mention trade-offs, always explain them in plain English. ## Optional advanced layer If useful, also include: - A scoring rubric for task complexity. - A confidence threshold for routing. - A simple policy matrix showing which model to choose for which task type. - A minimal version that could be built in a day versus a more advanced version for production. ## Final instruction Your output should read like a specification a product team could use to build the harness, not like a generic explanation of AI prompting.