People struggle with AI prompts because the model lacks background on their goals and progress. The solution is 'Context Engineering': creating an environment where the AI continuously accumulates user-specific information, materials, and intent, reducing the need for constant prompt tweaking.

Instead of manually crafting a system prompt, feed an LLM multiple "golden conversation" examples. Then, ask the LLM to analyze these examples and generate a system prompt that would produce similar conversational flows. This reverses the typical prompt engineering process, letting the ideal output define the instructions.

Tools like Notebook LM don't just create visuals from a prompt. They analyze a provided corpus of content (videos, text) and synthesize that specific information into custom infographics or slide decks, ensuring deep contextual relevance to your source material.

To write comedy professionally, you can't rely on inspiration. A systematic process, like 'joke farming,' allows for the reliable creation of humor by breaking down the unconscious creative process into deliberate, replicable steps that can be performed on demand.

To create a reliable AI persona, use a two-step process. First, use a constrained tool like Google's NotebookLM, which only uses provided source documents, to distill research into a core prompt. Then, use that fact-based prompt in a general-purpose LLM like ChatGPT to build the final interactive persona.

Moving beyond simple commands (prompt engineering) to designing the full instructional input is crucial. This "context engineering" combines system prompts, user history (memory), and external data (RAG) to create deeply personalized and stateful AI experiences.

Good Star Labs' next game will be a subjective, 'Cards Against Humanity'-style experience. This is a strategic move away from objective games like Diplomacy to specifically target and create training data for a key LLM weakness: humor. The goal is to build an environment that improves a difficult, subjective skill.

Developing LLM applications requires solving for three infinite variables: how information is represented, which tools the model can access, and the prompt itself. This makes the process less like engineering and more like an art, where intuition guides you to a local maxima rather than a single optimal solution.

AI serves two distinct roles in creative writing. First, it aids "divergent thinking" by creating a safe, non-judgmental space for brainstorming. Second, it assists "convergent thinking" by acting as a research assistant, wordsmith, and editor to refine a chosen concept.