Context Engineering: Give Models Exactly What They Need
Prompt & Context Engineering: leave with a versioned prompt template, structured-output schema and a small prompt regression test set.
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What this session would cover
Proposed outline — the mentor finalises the agenda once this topic is scheduled.
- 1Why prompt & context engineering matters — the common problem: Prompts are tweaked by feel, and nobody knows whether a change made things better.
- 2Core concepts in plain language: Task specification, system instructions, examples, prompt templates, structured outputs, context selection
- 3Going further: retrieval context, conversation history, context compression, tool descriptions, prompt testing, context-budget management
- 4Framework walkthrough: Transformer Architecture, Prompt → Retrieve → Generate → Verify, Evaluation Sets
- 5Practical workflow, built live: A versioned prompt template, structured-output schema and a small prompt regression test set.
- 6How to measure it: Groundedness, Answer accuracy on an eval set, Latency, Cost per request
- 7An illustrative case (a fictional example, not a client result), then live Q&A on your own situation
Who it's for
- • Students and freshers entering tech
- • Working developers and engineers
- • Tech leads and architects
You'd leave with
- A versioned prompt template, structured-output schema and a small prompt regression test set.
- A working understanding of Transformer Architecture and Prompt → Retrieve → Generate → Verify
- A short list of measures to track: Groundedness, Answer accuracy on an eval set, Latency
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