17 June 2026
GENERATIVE AI · UPDATED JUNE 2026
The specific techniques that move ChatGPT from generic to genuinely useful, and why most people never discover them.
Most people's experience of ChatGPT plateaus quickly: ask a question, get a reasonable but generic answer, move on. The gap between that and consistently excellent output isn't about the model getting smarter — it's almost entirely about how the request is framed. A handful of specific techniques account for most of the improvement, and none of them require technical skill.
The single highest-leverage change most people can make is simply being more specific. “Write a summary of this report” produces something generic because the model has no idea what you actually need: how long, for whom, emphasising what. “Write a 200-word summary of this report for a non-technical board member, emphasising financial risk and excluding technical methodology” produces something close to usable on the first try, because the model now has the constraints it needs to make good decisions rather than guessing at your intent.
This extends to format. If you want bullet points, say so. If you want a specific structure — problem, options, recommendation — specify it. ChatGPT will follow explicit structural instructions reliably; it has no way of inferring an implicit one.
For tasks with a particular style or format you want replicated, showing one or two examples of exactly what you're after — known as few-shot prompting — outperforms describing the style in the abstract almost every time. If you want emails written in a specific tone, paste in one example of that tone and ask the model to match it, rather than trying to describe “friendly but professional” in words. The model is far better at pattern-matching a concrete example than interpreting a subjective adjective.
For anything involving analysis, comparison, or multi-step logic, explicitly asking the model to work through its reasoning before giving a final answer (“think through this step by step before concluding”) measurably improves accuracy on complex tasks. This works because it forces the model to externalise intermediate reasoning rather than jumping straight to a plausible-sounding but unexamined conclusion. It's particularly valuable for anything involving numbers, logical deduction, or evaluating trade-offs between options.
A pattern worth normalising: treat the first response as a draft, not a final answer. “Make this more concise,” “the second point is weak, strengthen it,” “too formal, make the tone warmer” — each of these refinements typically takes seconds and compounds quickly. People who get the best results from ChatGPT tend to have short, iterative back-and-forths rather than spending five minutes crafting one perfect initial prompt.
Telling the model what to avoid is often as useful as telling it what to include. “Don't use corporate jargon,” “avoid bullet points, write in prose,” “don't include a generic disclaimer at the end” — these constraints meaningfully shape output and are easy to forget, because people default to only specifying what they want rather than what they don't.
Most prompting principles transfer across models, but a few things are worth knowing specifically about working with GPT-based models via the API rather than the consumer chat interface: system prompts (the instructions that set overall behaviour before the conversation starts) carry more weight than equivalent instructions placed mid-conversation, and GPT models respond well to explicit role framing (“you are an experienced corporate lawyer reviewing this clause”) in a way that measurably shifts the register and assumptions baked into the response.
For developers, the natural next step beyond better prompting is building applications on top of the OpenAI API directly — chatbots grounded in your own data via retrieval-augmented generation, automated workflows that call GPT models as one step in a larger pipeline, or tools that combine GPT's reasoning with structured business logic. This is a meaningfully different skill set from prompting well as an end user, involving API integration, error handling, and cost management at scale.
If you want this taught properly rather than picked up piecemeal, JBI Training runs courses covering both ends of this spectrum — prompting skills for everyday business use, and full developer training on building applications with the OpenAI API:
JBI Training delivers instructor-led AI and technology training to corporate teams across the UK and internationally, virtually and face-to-face.
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