Generative AI Roadmap 2026-27: LLMs, RAG and AI Apps

01

The plan

Generative AI roadmap at a glance

This is a month-by-month plan for a part-time learner who already knows basic Python. If you are starting from zero, add 4–6 weeks for Python and Git first. Every stage gives you something you can show — a working script, a prompt-driven tool, a RAG app or a deployed GenAI project.

Month-by-month plan — part-time estimate (10–12 hours a week)
Month	Learn	Practise / Build	Milestone you can show
Month 1	Python for APIs, JSON, async basics, Git, environment setup	Build a script that calls a public API, parses JSON and saves results	Small Python repo with a README and clean commits
Month 2	ML and deep learning fundamentals, transformers at concept level	Train a simple classifier; read and explain a transformer diagram	Notebook with a trained model and a written summary
Month 3	LLM fundamentals — tokens, embeddings, context, sampling, limitations	Compare two models on the same prompt; measure token usage and cost	Short report comparing model outputs and costs
Month 4	Prompt engineering, structured outputs, evaluation basics	Build a structured-output tool — summariser or classifier — with error handling	Working LLM app with system prompt, JSON output and evaluation
Month 5	Building with LLM APIs and open models (Hugging Face, local models)	Build a chat or QA app on top of an LLM API and an open model	Working app with two model backends and a simple UI
Month 6	RAG — chunking, embeddings, vector databases, retrieval, reranking, evaluation	Build a RAG app over your own PDFs with citations	RAG app that answers from your documents and shows sources
Month 7	Fine-tuning at concept level (LoRA), when to use it vs RAG	Compare RAG vs a small fine-tuned model on the same task	Report explaining when you would choose each approach
Month 8	Safety, guardrails, privacy, cost and latency	Add input/output validation, PII handling and cost tracking to your app	App with guardrails, logging and a cost dashboard
Month 9	Ship 3 GenAI apps + interview prep	Polish 3 projects with READMEs, evaluations and deployed demos	Portfolio with deployed GenAI apps and a one-page resume

Time estimate note: 6 months part-time is an estimate for someone studying 10–12 hours a week. A fast-track learner at 20+ hours a week can finish in about 3–4 months. Estimates are not guarantees — your pace depends on prior coding experience.

Visual roadmap: A designer-built SVG timeline of all nine stages, with a downloadable one-page PDF summary, is planned for this page. The text version above and the stage sections below cover the same content in accessible HTML.

02

Stage 1

Python, APIs, JSON, Git

Before you build GenAI apps, you need to be comfortable writing Python that talks to external services. This stage is about the plumbing: functions, HTTP requests, JSON parsing and version control.

Topics checklist
Topic	What to cover
Python functions and modules	Defining functions, type hints, imports, virtual environments, package management with pip
HTTP and APIs	GET/POST requests, headers, status codes, API keys and environment variables
JSON	Parsing and serialising JSON, nested structures, handling missing keys
Async basics	`async`/`await`, when it matters for API calls, basic error handling
Git and GitHub	Branching, commits, pull requests, README files

Free official resources: Python documentation (docs.python.org), MDN Web Docs for HTTP basics, GitHub Skills for Git.

Mini project: Write a Python script that calls a public API (for example a weather or currency API), parses the JSON response and saves a clean summary to a file. Commit it to GitHub with a README.

You are ready for the next stage when: you can write a Python script that calls an API, handles errors and saves structured output — without copying from a tutorial.

03

Stage 2

ML & deep learning fundamentals, transformers at concept level

You do not need to build models from scratch, but you need to understand what a model is doing and why it fails. This stage gives you enough ML and deep learning intuition to work with LLMs confidently.

Topics checklist
Topic	What to cover
Classical ML basics	Supervised vs unsupervised, train/test split, overfitting, evaluation metrics
Neural network basics	Perceptrons, activation functions, loss functions, backpropagation, optimisers
Transformers at concept level	Attention mechanism, self-attention, encoder-decoder, why transformers replaced RNNs for text
Pre-trained models	Why most production work starts from a pre-trained model, transfer learning basics

Free official resources: scikit-learn user guide, PyTorch tutorials, 3Blue1Brown (neural networks), Illustrated Transformer by Jay Alammar.

Mini project: Train a simple text classifier with scikit-learn. Write a short note explaining what a transformer is and why it matters for LLMs.

You are ready for the next stage when: you can explain what a transformer is at a high level and why fine-tuning a pre-trained model is usually better than training from scratch.

04

Stage 3

LLM fundamentals — tokens, embeddings, context, sampling, limitations

An LLM is not a search engine or a database — it is a next-token predictor. This stage teaches you what that actually means for the apps you will build.

Topics checklist
Topic	What to cover
Tokens and context windows	How text is tokenised, what a context window is, why long documents need chunking
Embeddings	What an embedding is, how similarity search works, why embeddings power RAG
Sampling parameters	Temperature, top-p, max tokens, how they change output
Limitations	Hallucinations, knowledge cutoffs, context limits, prompt injection risks

Free official resources: OpenAI API documentation, Anthropic documentation, Hugging Face course (LLM basics), Google Gemini API docs.

Mini project: Send the same prompt to two different models and compare output quality, token usage and cost. Write a short report on which one you would choose for your app.

You are ready for the next stage when: you can explain why an LLM hallucinates, what a context window is and why embeddings are needed for RAG.

05

Stage 4

Prompt engineering + structured outputs + evaluation basics

Prompt engineering is not about magic words — it is about writing clear instructions and forcing reliable output. This stage makes your prompts testable.

Topics checklist
Topic	What to cover
Prompt design	System vs user prompts, few-shot examples, chain-of-thought, instruction clarity
Structured outputs	JSON mode, schema-constrained output, validation with `pydantic` or similar
Evaluation basics	Task success rate, output quality checks, regression tests for prompts
Iteration	Changing one variable at a time, keeping a prompt log, measuring before and after

Free official resources: OpenAI prompt engineering guide, Anthropic prompt engineering docs, Hugging Face course (prompting).

Mini project: Build a text summariser or a document classifier that always returns valid JSON — for example {"summary": "...", "topics": ["..."]}. Add error handling for when the model returns malformed JSON.

You are ready for the next stage when: you can write a prompt that reliably returns valid JSON, and you can explain why a model gave a wrong answer and how you would change the prompt.

06

Stage 5

Building with LLM APIs and open models (Hugging Face, local models)

Now you move from prompts in a playground to real apps. This stage teaches you how to build with hosted APIs and open models you can run yourself.

Topics checklist
Topic	What to cover
LLM APIs	OpenAI, Anthropic, Gemini APIs — request structure, streaming, error handling, rate limits
Hugging Face	Transformers library, model hub, tokenisers, loading pre-trained models
Local models	Running small open models locally, when it makes sense vs API calls
App structure	Separating prompts, model calls and UI; environment variables; basic logging
UI options	Gradio, Streamlit — quick interfaces for GenAI demos

Free official resources: OpenAI API documentation, Hugging Face course, Gradio documentation, Streamlit documentation.

Mini project: Build a chat or question-answering app that can use either an LLM API or an open model. Add a simple UI so someone else can use it.

You are ready for the next stage when: your app can switch between an API model and an open model, and you can explain the trade-offs in cost, latency and quality.

07

Stage 6

RAG — chunking, embeddings, vector databases, retrieval, reranking, evaluation

RAG is how you make an LLM answer from your own documents instead of guessing. This stage teaches the full RAG pipeline — from chunking to reranking to evaluation.

Topics checklist
Topic	What to cover
Chunking	Fixed-size vs semantic chunking, overlap, metadata, handling tables and code
Embeddings and vector stores	Embedding models, vector databases (Chroma, FAISS, Pinecone), similarity search
Retrieval	Top-k retrieval, hybrid search, metadata filters, when retrieval fails
Reranking	Cross-encoder reranking, when it improves quality, cost trade-offs
Evaluation	Retrieval accuracy, answer quality, hallucination checks, citations

Free official resources: LangChain retrieval docs, LlamaIndex docs, Hugging Face embeddings guide, official vector store docs (Chroma, FAISS).

Mini project: Build a RAG app over your own PDFs (notes, manuals or a small document set). It should answer questions and show which chunk each answer came from.

You are ready for the next stage when: your RAG app answers questions from documents it has never seen, cites its sources and handles a question the documents do not answer.

08

Stage 7

Fine-tuning at concept level (LoRA) — when to use it vs RAG

Fine-tuning changes the model; RAG changes the context. This stage teaches you when each one makes sense, without spending weeks training a model you did not need.

Topics checklist
Topic	What to cover
When to fine-tune	Style, format, domain vocabulary vs new factual knowledge — which one fine-tuning actually fixes
LoRA and PEFT	What LoRA is, why it is cheaper than full fine-tuning, adapter basics
Dataset preparation	Instruction-response pairs, data quality, train/validation split
Fine-tuning vs RAG	Cost, latency, freshness of data, when to combine both
Evaluation	Comparing a fine-tuned model against a prompted model on the same tasks

Free official resources: Hugging Face PEFT docs, Hugging Face fine-tuning course, OpenAI fine-tuning guide.

Mini project: Compare a RAG app and a small fine-tuned model on the same task. Write a short report on which one you would choose and why.

You are ready for the next stage when: you can explain the difference between fine-tuning and RAG and pick the right one for a given problem.

09

Stage 8

Safety, guardrails, privacy (no PII in prompts), cost and latency

GenAI apps fail in ways chatbots do not — they leak private data, follow hidden instructions or burn through your API budget. This stage makes your app safe enough to ship.

Topics checklist
Topic	What to cover
Prompt injection	Hidden instructions in documents and tool outputs, input sanitisation, output validation
Privacy	No PII in prompts, redaction, data retention policies, on-prem vs hosted models
Guardrails	Input/output filters, allowed-topic lists, human approval for risky actions
Cost control	Token budgets per request, caching, cheaper models for simple tasks, monitoring spend
Latency	Streaming responses, batching, choosing the right model size

Free official resources: OWASP Top 10 for LLM Applications, OpenAI safety best practices, NIST AI Risk Management Framework.

Mini project: Add input validation, PII redaction and cost tracking to one of your earlier GenAI apps. Write a short note on what you would monitor in production.

You are ready for the next stage when: you can explain how a prompt injection would work in your app and what you do to prevent it.

10

Stage 9

Ship 3 GenAI apps + interview prep

The final stage turns learning into a portfolio. Three shipped GenAI apps with evaluations, guardrails and a deployed demo are what get you interviews for Generative AI Engineer and AI Engineer roles.

Apps to ship
App	What it does	Skills it shows
Document Q&A (RAG)	Answers questions from your own PDFs with citations	Chunking, embeddings, retrieval, evaluation
Summariser / classifier with structured output	Takes long text and returns a fixed JSON schema with summary and topics	Prompt engineering, structured output, error handling
Chat app with guardrails	A chat interface with input filters, PII redaction and cost tracking	Safety, guardrails, observability, cost control

Interview prep: Be ready to walk through your prompt design, your RAG pipeline, your evaluation numbers and one failure you fixed. Expect questions on hallucinations, prompt injection, cost per request and when fine-tuning is better than RAG.

You are ready to apply when: you have 3 deployed GenAI apps on GitHub, each with a README, an evaluation note and a live demo link.

11

Portfolio

Projects to build along the way

Each stage produces one project. By the end, these projects are your portfolio — they prove you can build, evaluate and ship GenAI apps, not just call an API.

Stage → project → what it proves
Stage	Project	Tools	What it proves
Stage 1	API script with clean output	Python, requests, Git	You can write working Python that talks to external services
Stage 2	Text classifier notebook	scikit-learn, pandas	You understand ML basics and model evaluation
Stage 3	Model comparison report	LLM APIs	You can explain token, cost and quality trade-offs
Stage 4	Structured text tool	LLM API, pydantic	You can get reliable JSON out of an LLM
Stage 5	Chat app with two backends	LLM API, Hugging Face, Gradio	You can build a real app on top of LLMs
Stage 6	RAG app over your documents	Embeddings, vector store	You can ground an LLM in private data with citations
Stage 7	RAG vs fine-tuning comparison	Hugging Face PEFT, LoRA	You understand when to choose each approach
Stage 8	Guardrailed chat app	Input filters, PII redaction, logging	You can make a GenAI app safe and affordable
Stage 9	3 deployed GenAI apps	FastAPI, Docker, cloud	You can ship GenAI apps to real users

12

Pace

Fast-track vs part-time plan

The roadmap is the same either way — only the pace changes. Choose the plan that fits your available hours, not the one that looks impressive.

Time estimates — after Python basics
Plan	Weekly effort	Estimated months	Best for
Fast-track	20+ hours a week	3–4 months	Full-time learners, career switchers with free time
Part-time	10–12 hours a week	5–6 months	Working professionals, students with college

Estimate note: These are estimates, not guarantees. Your pace depends on how much Python you already know and how much time you can protect each week.

13

Starting points

Generative AI roadmap for different backgrounds

01

Fresher (any degree)

Start with Python and APIs if you have never coded. Then follow all nine stages. Your portfolio projects matter more than your degree — build the RAG app and the guardrailed chat app first.

02

Non-IT / commerce background

Spend extra time on Stage 1 (Python, APIs, JSON). Use structured-output projects to build confidence early. The summariser/classifier app is a strong first portfolio piece.

03

Working professional

Go part-time (10–12 hours a week). Compress Stage 1 if you already write code. Focus on Stages 4–9 and ship three strong apps rather than five unfinished ones.

04

Career-gap returner

Treat the portfolio as your re-entry proof. Build the three Stage 9 apps with clear READMEs and evaluation notes. Add a short write-up of what you learned at each stage.

14

Two tracks

Generative AI for non-coders vs for developers

Not everyone needs to build from scratch. There are two valid tracks — one for people who want to use GenAI tools effectively, and one for people who want to build GenAI products.

Choose the track that fits your goal
Factor	Non-coder track	Developer track
Goal	Use GenAI tools confidently in your work	Build and ship GenAI applications
Core skills	Prompt engineering, structured outputs, evaluating AI tools, privacy basics	Python, LLM APIs, RAG, fine-tuning concepts, safety, deployment
Stages covered	Stages 3, 4 and 8	All nine stages
Typical output	Better prompts, reliable AI-assisted workflows, clear guidelines for your team	Working GenAI apps with RAG, guardrails and evaluations
Time estimate	4–6 weeks at 8–10 hours a week	5–6 months at 10–12 hours a week

15

Avoid these

Common mistakes on this roadmap

Skipping Python and API basics — every later stage becomes harder without them.
Thinking prompt engineering alone gets you a GenAI job — you need at least one shipped app.
Jumping to fine-tuning before RAG — RAG solves more problems at lower cost.
Skipping evaluation — an app without a success rate is a demo, not a project.
Ignoring safety — prompt injection and PII leaks are real risks in production.
Collecting certificates instead of shipping projects — employers interview on the portfolio.

16

After the roadmap

Jobs you can apply for at the end

At the end of this roadmap you can apply for Generative AI Engineer, AI Engineer and prompt-focused product roles. Glassdoor reports an average base salary of ₹9L for Generative AI Engineers in India (46 salaries, latest 14 Sep 2026).

Salary context — public sources (fetched 13 Oct 2026)
Role	Salary figure	Source and date
Generative AI Engineer	Average base ₹9L, range ₹4L–₹14.5L, average total pay ₹9.77L	Glassdoor, 46 salaries, latest 14 Sep 2026
AI Engineer	Average ₹10,71,910; Gurgaon ₹12.89L, Hyderabad ₹12.00L, Bengaluru ₹11.91L, Chennai ₹9.29L	Indeed, 50 salaries, updated 28 Sep 2026

Note: Salaries vary by city, company, skills and experience. Uncodemy does not guarantee any salary. These figures are for the related roles that GenAI skills are hired under.

17

Guided learning

Follow this roadmap with a trainer

Uncodemy does not currently run a dedicated Generative AI engineering program. The Artificial Intelligence course is the foundation — it covers Python, statistics, machine learning, deep learning, CNNs, RNNs and GANs with TensorFlow, PyTorch and scikit-learn. It does not cover LLM apps, RAG, LangChain or AI agents, so this roadmap is for self-study alongside it.

Uncodemy foundation programs
Program	Details
Artificial Intelligence	Artificial Intelligence — 200+ hours, 130+ live sessions. Fee and duration are not printed here because they conflict across Uncodemy pages; check the course page or speak to a counsellor.
AI using Python	AI using Python — Python-focused AI foundation. Fee and duration unverified.
Data Analytics with Gen AI	Data Analytics with Gen AI — ₹17,500 + GST, 6–7 months. Good for a data-focused starting point, not for LLM app building.

Ready to explore? Book a free counselling session, try a 2-class trial or use the AI-powered career guidance tool.

18

FAQs

Generative AI roadmap FAQs

Q1 How long does it take to learn generative AI? +

After Python basics, roughly 5–6 months part-time at 10–12 hours a week, or 3–4 months fast-track at 20+ hours a week. These are estimates, not guarantees. Your pace depends on prior coding experience and how much time you can protect each week.

Q2 Can I learn generative AI without coding? +

Yes, if your goal is to use GenAI tools confidently rather than build apps. The non-coder track covers prompt engineering, structured outputs, evaluating AI tools and privacy basics in 4–6 weeks. Building and shipping GenAI applications does require Python.

Q3 Do I need maths for generative AI? +

Only a working level. You need to understand probability, distributions and what an embedding is. You do not need to derive backpropagation by hand. If you plan to fine-tune models or work on model research, deeper maths helps.

Q4 Is prompt engineering enough for a GenAI job? +

No. Prompt engineering is one skill, not a complete role. Employers hiring for GenAI roles expect at least one shipped app with evaluation, and most expect Python plus RAG or API integration. Prompt-only roles exist but are fewer and usually product-focused.

Q5 Which GenAI projects should I build? +

Three apps work best: a document Q&A app with RAG and citations, a summariser or classifier with structured JSON output, and a chat app with guardrails and cost tracking. Each should have a deployed demo, a README and an evaluation note.

Q6 Is RAG or fine-tuning better to learn first? +

Learn RAG first. RAG solves more real problems at lower cost, is easier to update when data changes, and is what most GenAI apps use in production. Fine-tuning is worth learning after you understand RAG and know when it is the better choice.