The AI Engineer Cheat Sheet

The AI Engineer Cheat Sheet

All the tools you need to build a great AI-powered application

Emily Choi-Greene

At Clearly AI, we’re firm believers that “buy” is not always the answer for every organization. We also believe that everyone in the technology space should learn how to build AI applications and use AI to solve problems in their enterprise. We’ve put together a high-level cheat sheet to get you started, and we’re always happy to consult with you on the best ways to simplify your AI workflows.

Understanding the Icons

As security & privacy engineers, we’ve added notations for the best tooling from a privacy-preserving lens (e.g., companies that don’t train on your data), a security lens (e.g., companies with self-hosting options), and a compliance lens (e.g., companies with SOC2 certifications). We’ve also added notations for open source software and tooling with a generous free tier. The notation may not be complete (may be missing icons), but we did our best!

Step 0: Define the problem

What are you trying to achieve? What problem are you trying to solve? How are you planning to leverage AI?

This part is the most important, and the hardest area for us to give generic advice. If you’re looking for specific pointers on which security, privacy, and compliance problems are best addressed with AI, set up some time to chat.

Step 1: Model Selection

Pick the right model type for the task

The above image was intended for model selection for coding tasks, but the logic can generally be applied to most tasks.

Multi-model Support

As indicated above, you probably want to have your pick from a variety of model types, depending on your task.

Some options:

Major Labs

These are generally well-known and self-explanatory. Open AI and Anthropic both offer Zero Day Retention. Of note - there’s a major difference in using AI providers via API and via their Chat products like ChatGPT and Claude. The Terms of Use and System Prompts differ widely.

Some options:

Fine Tuning

Unsloth 📖 - the fastest way to train and fine-tune open source models

Step 2: Prompt Engineering

Prompt Frameworks

OOTB frameworks if you don’t want to interface with the LLMs directly.

Some options:

Structured Outputs

Structured outputs are hugely important for reliable responses for programmatic consumption.

Some options:

Memory

Some options:

Step 3: Evaluations

Evaluations are critical for determining whether your AI application is performing as expected. There’s a lot of evaluation frameworks out there, and a lot of investment in evaluation across AI companies.

Some options:

Feedback

DSPy 📖- prompt optimization

Step 4: Robust Integrations

A good AI application doesn’t exist in a silo. You probably want to enable interactions with webpages or other applications.

Tool Use

Some LLM providers support tool use out of the box today, such as Open AI’s Web Search. In addition to out of the box Tool Use capabilities, you can also register "tools" with your LLM in the system prompt by providing API calls that the LLM could choose to call.

Agent <> Web Page Interactions

Some options:

Integration Protocols

Goal: Create a Flywheel

Ways to create a positive feedback loop, make yourself more productive, and continue to accelerate your product.

Code editors

Some options:

UI Generation

Use AI to create dynamic user interfaces.

Some options:

Need help?

We're happy to chat with you further about building AI-based applications. Feel free to contact us at support@clearly-ai.com.