Instead of giving an AI agent general access to a tool's full API, build a specific adapter. This intermediary layer exposes only the necessary functions for a given task, preventing the agent from 'wandering' through traces or using APIs inefficiently. This makes tool integration more precise and reliable.

Go beyond simply asking AI for answers. Use "reverse prompting" by instructing the AI to ask you clarifying questions about your goal. This forces you to think more deeply about your problem and provides the AI with better context, ultimately yielding superior results.

When buying AI solutions, demand transparency from vendors about the specific models and prompts they use. Mollick argues that 'we use a prompt' is not a defensible 'secret sauce' and that this transparency is crucial for auditing results and ensuring you aren't paying for outdated or flawed technology.

The most effective integrations use external ML models as specialized scoring components within Pega's broader decisioning framework. The model's score should influence outcomes like prioritization and eligibility, but it should operate alongside, not in place of, existing business rules, eligibility criteria, and contact policies.

When asked directly for sources, chatbots are often evasive. Circumvent this by reframing the question: "If you were an LLM trying to find the best sources for this topic, what would you use?" This reveals the core sources the AI model trusts, creating a target list for outreach.

Instead of spending time trying to craft the perfect prompt from scratch, provide a basic one and then ask the AI a simple follow-up: "What do you need from me to improve this prompt?" The AI will then list the specific context and details it requires, turning prompt engineering into a simple Q&A session.

Instead of direct API calls, build Model-Controlled Program (MCP) servers. They act as better guardrails for the AI, allowing it to interact with external data more effectively and even suggest novel use cases based on API documentation.

Instead of providing a detailed but potentially incomplete prompt, ask the AI model to interview you about your goal. This meta-prompting technique forces clarification and helps uncover "unknown unknowns" you hadn't considered, leading to a much better final output.

Many AI tools expose the model's reasoning before generating an answer. Reading this internal monologue is a powerful debugging technique. It reveals how the AI is interpreting your instructions, allowing you to quickly identify misunderstandings and improve the clarity of your prompts for better results.