Last Updated on August 22, 2025

Prompt Engineering Mastery Series

(For GPT, Claude, Gemini – Cross-Model Techniques)

Module 1 – Fundamentals of Prompt Engineering

  • What is Prompt Engineering?
    • History & evolution (pre-GPT → modern LLMs)
    • Why prompting matters in enterprise & government contexts
  • Core Concepts
    • Tokens, temperature, top_p, max_tokens
    • Deterministic vs creative outputs
  • Simple Prompt Patterns
    • Direct questions
    • Instructional prompts
    • Role-based prompting (You are an expert Java architect…)
  • Hands-on Exercise: Write prompts to explain SQL joins to a school kid, a CS student, and a CTO.

Module 2 – Advanced Prompt Structuring

  • System vs User vs Assistant messages (for chat-based LLMs)
  • Chain-of-Thought prompting
  • Multi-step reasoning prompts
  • Zero-shot, one-shot, and few-shot prompting
  • Instruction hierarchy – avoiding prompt override issues
  • Hands-on Exercise: Use few-shot prompting to extract structured JSON from messy text.

Module 3 – Cross-Model Behavior (GPT, Claude, Gemini)

  • Strengths & Weaknesses of each model
    • GPT (best in reasoning + tools ecosystem)
    • Claude (strong in long-context + nuanced writing)
    • Gemini (good with multi-modal integration + Google ecosystem)
  • Prompt adaptations for each model
    • Wording sensitivity
    • Context length management
    • Safety filter handling
  • Hands-on Exercise: Convert a GPT-optimized prompt to Claude and Gemini without losing quality.

Module 4 – Context Management & Prompt Compression

  • When & why context windows matter
  • Context summarization for long conversations
  • Information chunking and sliding window techniques
  • Prompt compression to save tokens without losing meaning
  • Hands-on Exercise: Summarize a 10-page document into a 2K-token context prompt for Gemini.

Module 5 – Retrieval-Augmented Generation (RAG) Prompting

  • What is RAG?
  • Embedding generation & vector search
  • Prompt templates for RAG pipelines
  • Dealing with irrelevant retrieval noise
  • Hands-on Exercise: Build a mini RAG pipeline prompt to answer from Indian Railway tender documents.

Module 6 – Multi-Stage & Chained Prompting

  • Prompt chaining with LangChain / LlamaIndex
  • Iterative refinement prompts
  • Self-ask / ReAct framework prompts
  • Hands-on Exercise: Create a 3-step chain to analyze data, summarize, and draft a policy brief.

Module 7 – Persona & Role Play Prompting

  • Defining roles for better outputs
  • Contextual role memory
  • Hands-on Exercise: Make the AI act as an “Indian Govt. Tender Compliance Officer” and verify a bid document.

Module 8 – Evaluation & Prompt Optimization

  • Measuring prompt quality
    • BLEU, ROUGE, semantic similarity
    • Human eval metrics
  • A/B testing prompts
  • Prompt debugging techniques
  • Hands-on Exercise: Optimize a prompt to reduce hallucination in contract summaries.

Module 9 – Safety, Compliance & Guardrails

  • Avoiding model jailbreaks
  • Content filtering prompts
  • Bias reduction techniques
  • Government & enterprise safety concerns
  • Hands-on Exercise: Write a safe prompt for a chatbot handling citizen grievances.

Module 10 – Automation & Prompt Templates

  • Reusable prompt frameworks
  • Parameterised prompts
  • Prompt libraries for team collaboration
  • Hands-on Exercise: Create a library of 20 reusable prompts for policy document drafting.

Module 11 – Multi-Modal Prompt Engineering

  • Image + Text prompts (Gemini & GPT-4o)
  • Audio & video inputs
  • Hands-on Exercise: Give a scanned tender document as input and extract bidder eligibility in table format.

Module 12 – Building a Prompt Engineering Portfolio

  • Showcasing prompt skills for career growth
  • Creating GitHub + blog examples
  • Enterprise-ready prompt repository
  • Capstone Project: Build a multi-model prompt toolkit for your Law Firm ERP or RIMS project.

Bonus – Future of Prompt Engineering

  • From Prompting to Agentic AI
  • Self-healing prompts
  • LLM + AutoML integration

If we follow this Mastery Series, you’ll not only know “how to write prompts” — you’ll be able to design prompt systems for production-grade applications across GPT, Claude, and Gemini.