Prompt Engineering & AI Strategy
The advanced follow-on to Prompt Engineering for Business Productivity & Automation. In 4 weeks and 20 hours you move from writing good prompts to designing complete AI solutions for a business: engineered prompt templates with proper evaluation, reusable AI assistants with knowledge files and guardrails, tool-using agents that always stop for human approval, and retrieval-augmented generation (RAG) with LangChain basics. Every lesson is interactive, with 3D artwork you can turn and step through, and every module ends with a graded task set in a Surat business: a textile supplier, a retailer, a training institute and a service company. The core path needs no coding; an optional Python lab is there for technical learners. You learn the current way to build reusable assistants, including what OpenAI's announced Custom GPT retirement means for you. You finish with a capstone: an internal knowledge assistant with a working retrieval demo, a test suite, a security checklist, a cost estimate and a client presentation. The course builds practical skill and a portfolio; it does not promise a job, a salary or client income.
- Duration: 4 weeks
- Modules: 13
- Fee: Rs 18,999 (about Rs 3,166 a month on EMI)
- Field: AI & Automation
Roles this track prepares you for
- Prompt Engineer
- AI Content Specialist
- AI Strategy Analyst
Curriculum
- Advanced Prompt Engineering for Business - Week 1. Move from casual chatting to engineered prompts: a business objective, role, context, constraints, examples and an output format a team can reuse. Learn zero-shot and few-shot prompting, delimiters, conditional rules, JSON-style output schemas, versioning and adapting one prompt to different models.
- Engineered prompts versus casual prompts: the business brief behind every prompt
- Zero-shot versus few-shot: when examples make answers consistent
- Structured templates: delimiters, conditional rules and output schemas
- Prompt versioning and adapting one prompt to different models
- Structured Problem Solving and Reasoning Prompts - Week 1. Break a messy business request into stages a person can check. Learn what chain-of-thought prompting was, why you ask for decision criteria, evidence and calculations instead of a model's private reasoning, and why model guidance differs. Build decision trees with explicit assumptions and verification points.
- Decompose a messy business request into verifiable steps
- Chain-of-thought done right: ask for criteria and evidence, not hidden reasoning
- Decision trees, assumptions and verification checkpoints
- Prompt Evaluation, Reliability and Your Prompt Library - Week 1. Stop judging prompts by feel. Build a small evaluation set with expected outputs and acceptance criteria, score failures such as hallucinations and unsupported claims, rerun every test after each change and keep a versioned prompt library for communication, research synthesis, process documentation and decision support. Week 1 closes with Assignment 1: the Business Prompt Engineering Toolkit.
- Build an evaluation set: test cases, expected outputs and acceptance criteria
- Score hallucinations and unsupported claims, then fix the prompt
- Regression testing and a versioned prompt library
- Reusable AI Assistants: From Custom GPTs to Assistant Blueprints - Week 2. Decide when a reusable assistant beats a one-off prompt, then design one. Learn how Custom GPTs were configured, what OpenAI has announced about retiring them in December 2026 and moving to plugins, and how to write a platform-independent assistant blueprint that runs on any supported tool. Creating a personal Custom GPT is never required.
- Assistant, chatbot or plain prompt: discover the right use case
- Custom GPTs, their December 2026 retirement and what replaces them
- Write an assistant blueprint: identity, instructions, starters and boundaries
- Version, share and maintain an assistant specification
- Knowledge Files, Grounding and Guardrails - Week 2. Give an assistant the right documents and make it stay inside them. Prepare authoritative knowledge files, label versions and set a source hierarchy, write grounding and citation rules, protect sensitive information, recognise prompt injection and define refusals and escalation for out-of-scope requests.
- Prepare knowledge files: authoritative sources, versions and hierarchy
- Grounding and citations: answer only from approved sources
- Guardrails: prompt injection, sensitive data, refusals and escalation
- Assistant Acceptance Testing and Deployment Readiness - Week 2. Prove an assistant is ready before anyone relies on it. Design positive, negative and boundary tests, attack your own assistant with injected instructions and confidential requests, handle conflicting information and make a documented accept, fix or reject decision. Week 2 closes with Assignment 2: the Business AI Assistant Blueprint.
- Design a test matrix: positive, negative and boundary tests
- Attack your own assistant: injection, confidential requests and conflicts
- The acceptance decision and deployment readiness
- AI Agents and Visual Workflow Design - Week 3. Tell an assistant, a scripted workflow and a tool-using agent apart, and choose the simplest one that does the job. Learn the agent loop of goal, plan, tool and observation, then design workflows with triggers, validation, routing, branches, human review, error states, retries and completion criteria.
- Assistant, workflow or agent: choose the simplest architecture that works
- How an agent works: goals, tools, state and the agent loop
- Design a workflow: triggers, validation, routing and branches
- Error states, retries and completion criteria
- Tool Calling and Safe Business Integrations - Week 3. See how a model asks for a tool with a structured request, what the tool returns and where things go wrong. Write tool schemas, validation rules and permission boundaries using least privilege, keep credentials out of prompts, and plan audit logs and error recovery. Every integration in this course is simulated with fictional data.
- Tool calling explained: schemas, inputs and outputs
- Least privilege: permissions, credentials and validation rules
- Audit logs and error recovery for tool-using agents
- Human-in-the-Loop Agents and Safety Audits - Week 3. Decide what an agent may never do alone and put a person at those points. Read execution traces to spot wrong tool choices, loops and failures, set cost and step limits, and monitor agents after launch. Week 3 closes with Assignment 3: the AI Business Agent Workflow.
- Human approval checkpoints: what an agent must never do alone
- Read an agent trace: wrong tool choices, loops and failures
- Agent limits: cost caps, step limits and monitoring
- Retrieval-Augmented Generation Fundamentals - Week 4. See why a model alone cannot know your business documents and how RAG fixes part of that. Follow a document through ingestion, chunking, embeddings, vector similarity and retrieval to a grounded answer with citations, and learn the limits: RAG reduces unsupported answers but never guarantees accuracy.
- Why RAG: a model's memory versus your business documents
- Chunks and embeddings: how documents become searchable
- Retrieve, ground and cite: the full RAG pipeline
- LangChain Basics and a Small Retrieval Assistant - Week 4. Learn the LangChain building blocks and where LangGraph fits for state, branches and human approval. Build a small retrieval assistant on a synthetic dataset: the core path uses a visual simulation, the optional technical path a guided Python lab. Keyword and semantic retrieval are always labelled honestly, and the lab is an introduction, not advanced LangChain engineering.
- LangChain building blocks and where LangGraph fits
- Keyword versus semantic retrieval: build an honest classroom retriever
- Optional technical lab: a small retrieval assistant in Python
- RAG Evaluation, Security and Cost - Week 4. Judge a knowledge assistant on evidence: retrieval relevance, groundedness, citation correctness and unsupported answers. Defend it against conflicting or outdated sources, injected passages and access mistakes, then estimate tokens, latency and total cost honestly, including the case where AI saves nothing. Week 4 closes with Assignment 4: the Business RAG and AI Implementation Blueprint.
- Evaluate a RAG answer: relevance, groundedness and citations
- RAG security: poisoned sources, injected passages and access control
- Cost, latency and ROI: what an AI assistant really costs
- Capstone: Internal Business Knowledge Assistant - Week 4. Bring every week together for a fictional Surat service business buried in repeat internal questions. Using its document pack, take the problem from requirements through instructions, knowledge architecture, a working guided retrieval demo, an approval-controlled workflow, tests, security, cost and a client presentation. A small working demo is enough; it is an educational prototype, not a production deployment.
- Capstone brief: scope the problem, users and requirements
- Assemble the solution: blueprint, knowledge, workflow and approvals
- Test, cost and present your capstone to the client
Frequently asked questions
How long is the Prompt Engineering & AI Strategy course?
It runs for 4 weeks, with 13 modules. It is self-paced online learning from EasySkill Career Academy, Surat.
How much does the Prompt Engineering & AI Strategy course cost?
The fee is Rs 18,999, or about Rs 3,166 a month on EMI.
What will I build in Prompt Engineering & AI Strategy?
Every module ends in a graded practical task that AI checks in about 30 seconds, so you finish with a portfolio you can show to employers or clients.
Which roles does Prompt Engineering & AI Strategy prepare me for?
It prepares you to apply for roles such as Prompt Engineer, AI Content Specialist, AI Strategy Analyst. It does not guarantee a job or income.
Can I learn online, or do I have to visit a centre?
You can learn fully online. EasySkill also has centres in Adajan, Vesu and Bhestan, Surat, if you prefer to visit.
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