🦄 Cracking the Prompting Interview
Ready to level up your prompting skills? Join us for a deep dive into advanced prompting techniques that separate good prompt engineers from great ones. We'll cover systematic prompt design, testing tools / inner loops, and tackle real-world prompting challenges. Perfect prep for becoming a more effective AI engineer.
Project Details
Open in GitHubCracking the Prompting Interview
Ready to level up your prompting skills? Join us for a deep dive into advanced prompting techniques that separate good prompt engineers from great ones. We'll cover systematic prompt design, evaluation frameworks, and tackle real interview-style prompting challenges.
Video (1h23m - Available June 13, 2025 8 AM PST)
🎯 Key Takeaways
- Use Indexes for URLs & Citations: Provide content with simple IDs (e.g., [SOURCE_1]) and have the LLM output these IDs. Map them back programmatically to improve accuracy and reduce token load.
- Index-Based Diarization: For tasks like speaker diarization, have the LLM output the index of the dialogue turn and the identified speaker (e.g., {"dialogue_idx": 0, "speaker": "Nurse"}).
- Context & "Escape Hatches" for Classification: Provide relevant context upfront and include an "Other" or "Unknown" category to handle ambiguity.
- Reasoning via "Busted" JSON/Comments: Include LLM reasoning as comments or non-standard fields in structured output for easier debugging.
- Natural Code Generation (in JSON): Generate code within Markdown-style backticks as a string field in JSON for higher quality output.
- RTFP (Read The...Prompt!): Carefully review prompts for potential ambiguities that might confuse the LLM.
📝 Whiteboards
All Episodes
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Interruptible Agents
Anyone can build a chatbot, but the user experience is what truly sets it apart. Can you cancel a message? Can you queue commands while it's busy? How finely can you steer the agent? We'll explore these questions and code a solution together.
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A few weeks ago, the Manus team published an excellent paper on context engineering. It covered KV Cache, Hot-swapping tools with custom samplers, and a ton of other cool techniques. On this week's episode, we'll dive deep on the manus Article and put some of the advice into practice, exploring how a deep understanding of models and inference can help you to get the most out of today's LLMs.
Context Engineering for Coding Agents
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Evaluating Prompts Across Models
AI That Works #16 will be a super-practical deep dive into real-world examples and techniques for evaluating a single prompt against multiple models. While this is a commonly heralded use case for Evals, e.g. 'how do we know if the new model is better' / 'how do we know if the new model breaks anything', there's not a ton of practical examples out there for real-world use cases.
PDFs, Multimodality, Vision Models
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Implementing Decaying-Resolution Memory
Last week on #13, we did a conceptual deep dive on context engineering and memory - this week, we're going to jump right into the weeds and implement a version of Decaying-Resolution Memory that you can pick up and apply to your AI Agents today. For this episode, you'll probably want to check out episode #13 in the session listing to get caught up on DRM and why its worth building from scratch.
Building AI with Memory & Context
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Boosting AI Output Quality
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Building an AI Content Pipeline
Content creation involves a lot of manual work - uploading videos, sending emails, and other follow-up tasks that are easy to drop. We'll build an agent that integrates YouTube, email, GitHub and human-in-the-loop to fully automate the AI that Works content pipeline, handling all the repetitive work while maintaining quality.
Entity Resolution: Extraction, Deduping, and Enriching
Disambiguating many ways of naming the same thing (companies, skills, etc.) - from entity extraction to resolution to deduping. We'll explore breaking problems into extraction → resolution → enrichment stages, scaling with two-stage designs, and building async workflows with human-in-loop patterns for production entity resolution systems.
Humans as Tools: Async Agents and Durable Execution
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12-factor agents: selecting from thousands of MCP tools
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Policy to Prompt: Evaluating w/ the Enron Emails Dataset
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Designing Evals
Minimalist and high-performance testing/evals for LLM applications. Stay tuned for our season 2 kickoff topic on testing and evaluation strategies.
Twelve Factor Agents
Learn how to build production-ready AI agents using the twelve-factor methodology. We'll cover the core concepts and build a real agent from scratch.
Code Generation with Small Models
Large models can do a lot, but so can small models. We'll discuss techniques for how to leverage extremely small models for generating diffs and making changes in complete codebases.
Reasoning Models vs Reasoning Prompts
Models can reason but you can also reason within a prompt. Which technique wins out when and why? We'll find out by adding reasoning to an existing movie chat agent.
Large Scale Classification
LLMs are great at classification from 5, 10, maybe even 50 categories. But how do we deal with situations when we have over 1000? Perhaps it's an ever changing list of categories?