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.

Because we need to get smarter at using AI. We can't keep just building data centres and burning tokens.

Produce a clear product specification foundation for the AI harness: plain-English explanation, business goals, user personas, key problems, scope boundaries, success metrics, and the memorable vehicle simile explaining why different jobs need different model sizes. Define terms such as tokens, context, latency, cost, routing, and prompt trimming in non-technical language. Capture functional and non-functional requirements so a product team understands what must be built and why.

Design the harness architecture in detail, including the prompt analyser, token estimator, model router, prompt refiner, cost calculator, logging layer, quality checker, and feedback loop. Provide the end-to-end flow from incoming user request through classification, estimation, prompt optimisation, model choice, execution, logging, and response delivery. Include component responsibilities, data exchanged between components, failure handling, and accessible pseudocode or structured process steps.

Create the practical decision system for choosing model tiers. Deliver a task complexity scoring rubric, confidence thresholds, fallback rules, policy matrix by task type, and trade-off guidance for cost, latency, and quality. Include rules for simple extraction, rewriting, summarisation, classification, coding, ambiguous reasoning, high-stakes work, long-context work, and cases where the harness should ask a clarifying question or escalate to a stronger model.

Define how the harness estimates input and output token usage before calling a model, how it chooses target output length, and how it rewrites or trims prompts without losing important constraints. Include concrete prompt-cleaning rules for duplication, irrelevant context, vague instructions, conflicting requirements, tone preservation, output format preservation, and context prioritisation. Provide before-and-after examples and safeguards against over-trimming.

Specify the logging and analytics layer: predicted versus actual input tokens, output tokens, chosen model, estimated cost, actual cost, latency, routing confidence, prompt changes, task category, user feedback, retries, failures, and quality outcomes. Design how historical logs improve future routing and token estimates. Include dashboards, review workflows, privacy cautions, evaluation labels, alerting rules, and examples of how the system learns from mistakes.

Assemble the final build-ready specification for a product team. Include a minimal one-day MVP, a production roadmap, optional API/data schemas, example user requests, classification examples, selected models and reasons, shortened prompt versions, cautions, and rollout recommendations. Polish the document into the exact requested business-friendly structure while keeping it decisive, practical, and concrete.