AI for Optimization

This section documents AMPL’s AI-related resources and how to use them effectively for optimization work.

We provide AMPL-focused chat assistants for the main LLM ecosystems, including platforms from OpenAI, Google, and Anthropic. The goal is to help optimization practitioners get faster, more relevant guidance for AMPL modeling, solver usage, and AMPL+Python workflows, directly in the AI tools they already use.

Using these assistants through your existing provider account can also be convenient from a cost and access perspective. If you already subscribe to one of these platforms, you can typically use AMPL custom chatbots within that subscription environment, instead of adding a separate standalone AI tool. In many cases, paid tiers on these providers offer better model quality, higher usage limits, and improved responsiveness.

Why using chatbots for optimization?

Chat bots bridge the gap between complex optimization problems and reliable and efficient code. Whether you are a student writing your first .mod file or a professional tuning solver parameters, AMPLbot provides instant, professional-level guidance.

• Faster onboarding: get help with syntax, environment setup, and amplpy integration in seconds.

• Modeling best practices: learn how to translate real-world constraints or complex business rules into elegant mathematical formulations using AMPL’s latest MP features.

• Model explainability: interpret your mathematical model, understand translation from real-world requirements to mathematical constraints, learn why the model works, analyze sensitivity or diagnose infeasibility of your data.

• Solver tuning: receive advice on selecting and configuring your solvers, including open-source and commercial solvers.

Code generated by custom bots like AMPLbot are more reliable than using generic models. In any case, make sure you understand what you are adding to your model.

Available flavors

We have deployed AMPL-focused assistants across leading AI platforms so you can pick the one that fits your workflow:

• AMPLbot in ChatGPT

• Claude Skill for AMPL

• NotebookLM for AMPL

• Gemini assistant

Performance is not uniform across AI platforms, and the underlying language model is usually the main factor affecting response quality, reasoning, and accuracy.

Since there is currently no standardized way to interact with Generative AI systems, we provide AMPL assistants across multiple ecosystems so users can choose the platform that best fits their workflow.

These assistants include AMPL-specific knowledge and guidance, but hallucinations and incorrect responses may still occur. Users should always validate generated models, code, and recommendations.

How to get better answers

Generic prompts produce generic output. Include concrete model context, explicit descriptions of the requirements, and if possible, verifiable examples.

• State the optimization type: LP, MIP, NLP, MINLP, CP, Multi-Objective….

• Provide data shape and units: indices, dimensions, and expected value ranges.

• Request diagnostics explicitly: ask for infeasibility checks, bound-tightening ideas, and alternative formulations.

• Ask for executable artifacts: a .mod file plus an amplpy script and a short validation checklist.

• Ask for assumptions to be explicit and numbered.

• Ask for a post-solve validation checklist (feasibility, bound checks, sanity checks).

Example of a strong prompt:

“Build a weekly workforce scheduling MIP. I have 7 days, 3 shifts/day, 42 employees, and coverage requirements by shift. Hard constraints: labor law rest windows and max weekly hours. Soft constraint: fairness in weekend shifts. Return a clean .mod model and an amplpy script that loads pandas DataFrames, solves with HiGHS, reports IIS if infeasible, and prints staffing shortfalls.”