Coding with LLMs was easier after understanding their limitations. 1. They don't know what they are saying until they have said it. 2. Your inputs and its out

Coding with LLMs was easier after understanding their limitations. 1. They don't know what they are saying until they have said it. 2. Your inputs and its outputs help make the next message. 3. LLMs are not suited for information retrieval like databases and search engines. LLMs excel at reasoning and predicting subsequent text based on given context. Their strength lies in their ability to generate relevant and cohesive responses. To optimize results, outline clear rules, strategies, or ideas for the LLM to follow. This helps the model craft, revise, or build upon the established context. Starting with a precise query and introducing rules or constraints incrementally can help steer the model's output in the desired direction. Avoid zero-shot queries as these can lead to the model generating unexpected or unrelated responses. Be cautious while seeking pre-calculated or non-derived answers. Some instruction-tuned models might output incorrect solutions, as they are trained to respond to certain queries without proper context or information. also, this is my biggest gripe no fault of ours of course: don't seek pre-calculated or non-derived answers. I've seen some of the demonstration data that people are using to train instruction-tuned models and are being taught to respond by making up answers to solutions it shouldn't try to compute. Btw, the output is wrong. { "instruction": "What would be the output of the following JavaScript snippet?", "input": "let area = 6 * 5;\nlet radius = area / 3.14;", "output": "The output of the JavaScript snippet is the radius, which is 1.91." }, https://github.com/sahil280114/codealpaca/commit/0d265112c705f88848783dfdf9bdca9df64d8e35#diff-0585315a2292ca9c3186c41e52ce9427a39b5f595d4bee6aae15e9443c95051bR25 https://github.com/sahil280114/codealpaca/commit/0d265112c70...