Iterate and experiment with your agentic workflows, >5x faster

Prompt engineering is the practice of writing clear and effective instructions that guide LLMs to produce outputs that meet your requirements. Models are non-deterministic and may return different results for the same input. Carefully crafting and iterating on prompts is essential to ensure that responses reliably meet quality, safety, and business requirements.

With Maxim's prompt management platform, you can operationalize this entire process at scale. You can iterate, version, and evaluate prompts across models, parameters, tools, etc. You can run these experiments against an eval dataset on metrics you care for, and automate this process to catch regressions/make improvements, all while ensuring seamless cross-functional collaboration and rapid experimentation.

Maxim AI offers a centralized Prompt Playground that enables engineering, product, and QA teams to collaborate effectively on prompts.

The platform’s version control system automatically tracks every change with a complete audit trail, including author details, comments, and modification history. You can run comparisons side-by-side against different versions on the playground, or run evals over a dataset comparing different versions to assess quality and performance. Maxim decouples prompts from application code, allowing teams to use one-click deployment with custom rules and roll out the best version without needing an app redeployment.

Teams can also organize prompts using folders, subfolders, and custom tags for easy discovery

(See: You can learn more about prompt versioning here.)

Evaluations on Maxim entail three core components:

• The system you’re evaluating: You can evaluate individual prompts or end-to-end agents. Maxim allows you to run detailed comparison experiments across different prompts, models, parameters, contexts, and tool combinations.
  
  
• Datasets: You run your evals against curated datasets. Maxim enables you to create multi-modal datasets and evolve them over time leveraging production logs and human feedback. You could also use synthetic data generation for dataset creation.
  
  
• Evaluators: These are metrics tuned to your specific outcomes that you would use to evaluate agent quality. You can create your own custom metrics or leverage Maxim’s Evaluator Store of pre-built multi-modal evaluators. The platform also has deep support for human-in-the-loop workflows to help you balance auto-evals with nuanced human evaluations for AI quality.

You can execute large-scale evals using these components through an intuitive no-code interface (ideal for Product Managers) or automate them via CI/CD workflows using our Go, TypeScript, Python, or Java SDKs. Additionally, you could run retroactive analysis to generate comparison reports uncovering trends over time and optimize your agents.

(See: Learn more about prompt evaluation here.)

Yes, Maxim enables you to build and experiment with complex agentic workflows using its No-Code Agent Builder. This visual interface allows you to orchestrate multi-step logic without writing code by leveraging existing prompts from your Prompt CMS. You can chain these prompts together on a canvas, mapping the output of one step to become the input variable for the next, and seamlessly integrate tool nodes (for API calls and function calls), code blocks (for custom scripts), and conditional logic. You can run evals on these end-to-end agents and deploy them directly from the platform.

Yes, Maxim AI supports evaluating prompts with multimodal inputs across both the Prompt Playground for interactive experimentation and Evaluation Runs for batch testing. You can iterate on prompts using diverse data types (including text, images, audio, and documents) directly in the Prompt Playground. For scale, you can run Evaluation Runs against datasets containing multimodal fields, ensuring your prompts perform consistently.

Yes, you can leverage Prompt Partials on Maxim. They are reusable snippets of prompt content such as tone guidelines, safety rules, or formatting instructions that can be created once and used across multiple prompts. Instead of rewriting the same instruction for every agent, teams define and version it centrally (e.g., {{partials.brand-voice.v1}}) and inject it wherever needed.

With Maxim’s granular role-based access control, teams can ensure that only specific members can create and edit prompt partials, while the rest of the team uses them as part of prompt experimentation. This enables effective collaboration across teams, especially between engineering and product, while ensuring the integrity of prompt components that should not be modified by all team members.

(See: Learn more about prompt partials here.)