Prompt Engineering
The model cannot read your product, database, college form, or user intent unless you pass the right parts in the prompt.
Good context gives the model the facts it must use, the rules it must follow, and the limits it must not cross.
Think of context as the minimum packet a smart intern needs to do the task correctly.
For an AI feature, that packet usually has four parts: user goal, relevant data, product rules, and output audience.
0 of 8 lessons done · proved by a submitted hackathon project
Do each one yourself, then tap it to tick it off. The ticks are only a checklist for you: they are not marked or scored.
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Beginners paste the full PDF, full chat history, or whole database row. This makes the prompt slower, harder to debug, and easier to confuse.
Do not add context because it feels useful. Add it because the answer would be wrong without it.
Use stable context for rules that rarely change. Use task context for the current user case.
This makes testing easier. When a prompt fails, you can see whether the problem is the rule layer or the case data.
Suppose your hackathon project helps hostel students choose a meal from a mess menu. The model should not invent items or prices.
Your prompt can pass a small context object instead of the full menu app.
SYSTEM CONTEXT
You help students choose from the hostel mess menu.
Use only the menu items provided in TASK CONTEXT.
Do not invent food items, prices, discounts, or nutrition facts.
If the menu does not contain a suitable option, say so.
Keep the answer under 80 words.
TASK CONTEXT
User goal: Find a vegetarian dinner option under ₹80.
Audience: College student in a hostel.
Date: Tuesday dinner.
Menu items:
- Veg thali, ₹70, vegetarian
- Egg curry rice, ₹85, non-vegetarian
- Paneer roll, ₹95, vegetarian
- Lemon rice, ₹60, vegetarian
USER MESSAGE
I want something filling and veg, but not expensive.After the model replies, check whether every claim came from the context. If it mentions a discount, calorie count, or item not listed, your prompt allowed guessing.
Also check whether the model used the user goal. A technically correct answer can still be bad if it ignores budget, date, language, or audience.
Do each one yourself, then tap it to tick it off. The ticks are only a checklist for you: they are not marked or scored.
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Answer the quick check to finish this lesson.