Skill Guide

Prompt engineering and LLM application patterns

The systematic design of input prompts and integration patterns to elicit specific, reliable, and high-quality outputs from Large Language Models (LLMs) within software applications.

It directly impacts product quality, development velocity, and operational cost by transforming a probabilistic model into a deterministic tool. Mastery enables the creation of scalable, maintainable, and innovative AI-powered features, securing competitive advantage and driving measurable ROI.

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How to Learn Prompt engineering and LLM application patterns

Focus on: 1) Core prompt structures (Role, Context, Instruction, Format), 2) Understanding LLM parameters (temperature, top_p, max_tokens) and their trade-offs, 3) Basic iteration and output evaluation using frameworks like CLEAR (Concise, Logical, Explicit, Actionable, Relevant).

Move to practice by designing multi-step prompts for real workflows (e.g., data extraction + formatting). Learn and apply patterns like Chain-of-Thought (CoT), Few-Shot, and Self-Consistency. Avoid common pitfalls: prompt injection, over-reliance on a single prompt, and failing to systematize version control for prompts.

Master by architecting LLM application systems. Focus on: 1) Implementing prompt orchestration frameworks (e.g., LangChain, LlamaIndex) with guardrails, 2) Designing evaluation pipelines with quantitative metrics (e.g., accuracy, latency, cost per query), 3) Aligning LLM strategy with business KPIs and leading cross-functional teams in prompt engineering best practices.

Practice Projects

Beginner

Project

Build a Structured Data Extractor

Scenario

Extract specific fields (name, date, amount) from unstructured email text and output them as clean JSON.

How to Execute

1) Define the JSON schema for output. 2) Craft a prompt with clear role ('You are a data parser'), context (sample email), and explicit instruction to output in the defined JSON format. 3) Test with 5-10 varied email examples, iterating on prompt clarity to reduce errors. 4) Document the final prompt and its success rate.

Intermediate

Project

Implement a Multi-Stage Summarization & Q&A System

Scenario

Given a long technical document, create a system that first generates a structured summary, then allows a user to ask specific questions about the summary content.

How to Execute

1) Use a prompt with Chain-of-Thought to generate the summary, instructing the model to extract key arguments and data points. 2) For the Q&A stage, implement a retrieval-augmented generation (RAG) pattern: index the summary chunks, retrieve relevant ones for a question, and use a precise 'answer with source' prompt. 3) Build a simple script or use an orchestration tool to chain these steps. 4) Evaluate the end-to-end system's accuracy on factual recall and coherence.

Advanced

Project

Architect a Prompt-Driven Code Review Pipeline

Scenario

Design and deploy a CI/CD-integrated system where an LLM performs initial code reviews on pull requests, highlighting potential bugs, security issues, and style deviations based on internal guidelines.

How to Execute

1) Create a master prompt template with dynamic slots for code diff, language, and internal coding standards. 2) Implement a few-shot pattern with curated examples of 'good' and 'bad' code reviews from senior engineers. 3) Build a feedback loop where developer votes on review quality fine-tune the prompt or the few-shot examples. 4) Integrate the service with GitHub/GitLab APIs, monitor latency, cost, and adoption metrics, and establish governance for prompt updates.

Tools & Frameworks

Orchestration Frameworks

LangChainLlamaIndexSemantic Kernel

Used to chain LLM calls, integrate with external tools (databases, APIs), and manage complex prompt workflows. Essential for building production-grade applications beyond simple single-turn interactions.

Version Control & Testing

PromptLayerWeights & Biases (W&B) PromptsHumanloop

Platforms for logging, versioning, evaluating, and monitoring prompts in production. They provide A/B testing, cost tracking, and performance analytics critical for iterative improvement.

Prompting Methodologies

Chain-of-Thought (CoT)Retrieval-Augmented Generation (RAG)Meta-Prompting

CoT improves reasoning on complex tasks. RAG grounds model answers in external, up-to-date knowledge. Meta-prompting involves using an LLM to generate or refine prompts, useful for scaling prompt engineering efforts.

Interview Questions

Answer Strategy

Use a structured debugging framework: 1) **Triage**: Analyze error logs to categorize failures (e.g., hallucinations, format violations, off-topic). 2) **Root Cause**: For each category, inspect the prompt's clarity and the input data quality. 3) **Solution**: Apply targeted fixes-add negative examples for format issues, inject more context for hallucinations, or implement a validation layer. 4) **Measure**: Re-deploy with monitoring to track the impact on error rate. Example: 'I'd start by bucketing the errors to see if they're systematic. If many are formatting failures, I'd add explicit 'Do not...' instructions and a few adversarial examples to the few-shot set, then A/B test the new prompt.'

Answer Strategy

Testing communication and strategic thinking. Sample: 'I framed it as an engineering trade-off. I built a small proof-of-concept comparing the time and maintenance cost of a complex regex parser versus a well-crafted prompt with a validation function. The prompt solution was more adaptable to new formats and had a faster iteration cycle. I presented the data on development speed and long-term maintainability, which aligned with our team's goals for agile feature delivery.'