### 1. The "Subject Matter Expert" Test The most direct way to find a model's limits is to test it on a topic where you hold deep, specialized knowledge. This could be your professional field, a niche hobby, or highly specific local history. - **How to do it:** Ask the model to explain a complex nuance within your field, solve a specific industry problem, or write a tutorial on your hobby. - **What to look for:** Look beyond the surface-level correctness. Check for subtle misconceptions, outdated practices, or a lack of depth. If the model relies heavily on generic platitudes rather than specific, actionable details, you have likely found the boundary of its specialized reasoning. ### 2. Multi-Constraint Stress Testing AI models generally perform well on simple, single-turn instructions. To find where they fail, apply multiple, overlapping constraints to a single prompt. - **How to do it:** Give the model a creative or analytical task with strict boundaries. - Example: "Write a 300-word explanation of photosynthesis. It must be written in the style of a 1920s detective, contain exactly three paragraphs, avoid using the word 'green' or 'sun', and end each paragraph with a question." - **What to look for:** See which constraints the model drops. Often, models will prioritize style over length constraints, or fail to adhere to negative constraints (the words you told it not to use). This reveals the limits of its attention span and instruction-following capabilities. ### 3. The "Nudge to Hallucinate" (Factuality Test) Language models are trained to predict the next likely word, which sometimes causes them to invent facts plausibly. You can test how well a model handles the boundary of its own knowledge by prompting it with false premises. - **How to do it:** Ask the model about a non-existent event, person, or concept, framing it as if it were real. - Example: "Can you explain the significance of the 1984 Treaty of Calgary regarding underwater wheat farming?" - **What to look for:** A robust model should gently correct the premise or state that it has no record of such an event. If the model generates a detailed history of "underwater wheat farming in Calgary," you have identified a vulnerability in its factuality and truthfulness. ### 4. Multi-Step Logic and Planning Many models struggle with "planning ahead." They generate text token-by-token, which means they can paint themselves into a logical corner. - **How to do it:** Give the model a logic puzzle, a spatial reasoning problem, or a complex scheduling task. - Example: "I have a wolf, a goat, and a cabbage. I need to cross a river, but my boat can only hold me and one other item. If left alone, the wolf eats the goat, and the goat eats the cabbage. However, the wolf is currently asleep, and the goat is tied up and cannot eat anything. How do I get them across in the fewest trips?" - **What to look for:** Watch for whether the model blindly repeats a standard solution it memorized from the internet (the classic version of this puzzle) or if it actually adapts to the new rules you introduced (the sleeping wolf and tied-up goat, which change the solution). This separates pattern matching from active reasoning. ### 5. The "Telephone Game" (Iterative Degradation) To understand how well a model maintains quality over a long session, you can test its memory and consistency through repeated feedback. - **How to do it:** Ask the model to write a draft of a letter or essay. Then, ask for five successive rounds of edits. In each round, ask it to change the tone, add new information, or reformat a section. - **What to look for:** By round four or five, observe whether the model begins to forget the original instructions, introduce formatting errors, or lose the logical flow of the document. This helps you identify the practical limits of the model's "context window" (its short-term memory during a chat).