Generative AI Course in Maninagar, Ahmedabad

Generative AI here means practical workflows: foundation models, prompting, careful context, structured outputs and evaluation before you trust the answer. At Computer Education And Cybernetics (CEC), Tanvi Darji reviews your eval score sheet — not only one fluent reply. The lab is ~2 minutes from maninagar railway station.

Book counselingSee the eval-first loop

Or reach us directly

+91 75740 10176 · info@cecyours.org

Eval first — then change the prompt

  • JobOne sentence: what the model must produce, for whom, and what “done” looks like.
  • ContextOnly the facts and instructions that fit this turn — drop the rest on purpose.
  • ShapeAsk for structured fields (JSON or a fixed list) so you can check answers in code or by hand.
  • Eval setFive fixed cases with known good answers — including one that must refuse when facts are missing.
You write
Eval cases
You pack
Context
You leave with
Score sheet
You start with
Counseling

What do you practise in generative AI?

Six pieces of a workflow. Evaluation sits with prompting — not as a final polish.

Generative AI is building useful workflows with foundation models through prompting, context packing, structured outputs, evaluation and application steps people can follow. Practice means scoring fixed test cases — including refuses — before you trust a fluent answer. Computer Education And Cybernetics (CEC) reviews that evaluation habit in mentor-checked project packets.

  • Foundation models

    Large models that generate text, code or images by predicting likely next pieces. You treat them as capable assistants that still guess when context is thin.

  • Prompting

    Clear task, audience, format and an example. Prompt changes are experiments — you re-run the same eval set after each edit.

  • Context packing

    What the model can see this turn: instructions, documents, short history. If a fact is missing from context, fluent invention is still a failure.

  • Structured outputs

    Named fields you can validate — title, bullet points, source note — instead of free paragraphs alone. Bad shapes get rejected before anyone trusts them.

  • Evaluation

    A fixed question set with expected behaviours: correct cite, refuse when unsupported, schema pass. Scores go in your project note.

  • Application workflows

    The path a person follows: upload or paste → model call → review → accept or refuse. Workflow design is part of generative AI practice here, not an afterthought.

Practice task

Before any live model call, write five eval cases for one document task — including one that must refuse. What we do with you here: Tanvi checks that list before you spend time polishing a single prompt.

A worked example: neat JSON, invented discount

Input → what you look at → decision → what you write down. This is the evaluation habit we practise with you.

  1. The workflow

    Summarise a six-page sandbox fee policy into JSON: three bullets, fee_amount, and source_paragraph. Mentors also want a refuse path when the policy does not mention the asked course.

  2. First broken run

    You paste the whole PDF into the prompt once. The model returns neat JSON and invents a “weekend sibling discount” that is not in the document. Without an eval set, the demo looks fine.

  3. What you look at

    source_paragraph is vague. Eval case “Python fees only” passes. Eval case “weekend discount?” should refuse — it invents instead. Context was too long and poorly selected.

  4. The decision

    Write five eval cases first. Pack only the fee table paragraphs into context. Instruct refuse when no paragraph supports the claim. Reject JSON with empty source_paragraph.

  5. After the fix

    Four cases pass. The weekend case refuses cleanly. One borderline case needs a clearer paragraph id — noted as a labelling fix, not a prompt gamble.

  6. What you write down

    Job sentence, context rule, schema, eval scores before and after, and the refuse example. Tanvi reviews that packet — not a single lucky chat reply.

For the citywide building blocks including tool use, see the generative AI course in Ahmedabad. For coding around model APIs, see the GenAI developer course in Ahmedabad.

Where is generative AI practice heading?

Fluency rises. Missing context still invents. What we do with you here: every workflow keeps an eval sheet and a refuse example.

  • Models get stronger; invented gaps still happen

    Newer models write more fluently. When your context omits a fact, they can still invent. Fixed eval cases and refuse rules stay the safety net.

  • Structured outputs become product defaults

    More tools ask for schema-shaped replies. You still design the fields and score them — the schema does not invent missing policy lines.

Where generative helpers still fail

  • It summarises confidently when the needed paragraph never entered context
  • It returns valid JSON while source fields are empty or invented
  • It drafts a new prompt that breaks old eval cases you never re-ran

Generative AI practice is shifting toward stronger models and schema-shaped outputs, while invented answers from thin context remain a common failure. The stable skills are careful context packing, structured validation and fixed evaluation cases. Computer Education And Cybernetics (CEC) trains learners to keep those habits in every generative workflow.

How we teach generative AI

About 80% practical — with mentor review of your eval sheet, not only a chat screenshot.

  1. 1. Counseling maps your starting rung

    Prompt practice, document workflows or Python-backed GenAI — school learners of any grade start with counseling first.

  2. 2. Tanvi Darji reviews your eval set

    She teaches data science, machine learning and artificial intelligence. She sends the project back if you only show one lucky reply.

  3. 3. You pack context and structure output

    About 80% of training time is practical. You change one prompt or context rule at a time and re-score the same five cases.

  4. 4. You leave with a workflow packet

    Job sentence, context rule, schema, scores and a refuse example — portfolio evidence for mentoring and interviews.

At Computer Education And Cybernetics (CEC), generative AI starts with counseling, then job design, context packing, structured outputs and scored evaluation reviewed by mentors such as Tanvi Darji. Learners leave with a workflow packet they can explain in interviews. We stay with you until you get a job, based on your performance in training, projects and interviews.

AI Development using Python (Job Oriented)

Catalogue home for generative AI workflows inside the wider AI developer path.

View course

Generative AI & LLM Development

When counseling places generative models and LLM practice beside application wiring.

View course

Who is this generative AI path for?

College learners, career changers and working professionals ready for scored generative workflows.

  • College students

    Move from casual chatting to a generative workflow with context rules, structured output and scored test cases.

  • Career changers

    Counseling maps whether prompting practice or Python-backed GenAI paths come first inside AI Development using Python.

  • Working professionals

    Add generative steps to study or office workflows with evaluation you can show a manager — not only a demo screenshot.

Placement support and certificates

A workflow packet with eval scores and a refuse example shows employers you verify generative output carefully.

How we stay with you

  • We stay with you until you get a job, based on your performance in training, projects and interviews
  • Our 5-step placement preparation covers your resume, portfolio, communication and mock interviews
  • Course completion certificates are issued after you finish the practical requirements

Visiting CEC Maninagar for generative AI counseling

~2 minutes from Maninagar Railway Station. BRTS, AMTS serve the area. Learners also travel from Kankaria, Isanpur, Ghodasar, Khokhra, Meghaninagar, Danilimda. Hours: Mon – Sat: 8:45 AM – 7:15 PMSun: 10:00 AM – 6:00 PM.

  • CEC Maninagar at Gopal Tower near Maninagar Railway Station
  • CEC Maninagar computer lab

  • Shilpi Sharma

    Software Faculty · 2+ years

    CCC · Tally Prime · Prompt Engineering

  • Tanvi Darji

    Software Faculty

    MERN Stack Development · Data Science · Machine Learning · Artificial Intelligence

Full address and map: CEC Maninagar location details. For LLM call contracts citywide, see LLM app development in Ahmedabad.

Our three centres in Ahmedabad

For classroom admission we warmly welcome you and your parents to visit — Maninagar is featured first below because this page is for learners choosing that centre. Please choose whichever centre is easier for you. Outside Ahmedabad? We offer online admission with a counseling call.

Related guides

Questions about generative AI

  • What is generative AI?

    AI that creates new text, code or images from a prompt and context, using foundation models. Practical work covers prompting, packing context, structured outputs, evaluation and application workflows — not only chatting once.

  • How does a generative AI workflow work here?

    You define the job, pack only relevant context, ask for a structured shape, then score a fixed eval set — including a case that must refuse when facts are missing. Mentors review that score sheet before the project counts.

  • Why write evaluation cases before changing the prompt?

    Prompt edits can fix one example and break three others. A small fixed set catches invented claims and empty source fields. In our fee-policy example, the weekend-discount case exposed invention the happy demo hid.

  • How is this different from the citywide generative AI page?

    The Ahmedabad generative AI guide covers the full building blocks including tool calls. This Maninagar page focuses on evaluation-first workflows — context packing and scored test cases — with local counseling at Gopal Tower.

  • Do I need Python before generative AI?

    Some learners start with prompting and evaluation on documents; others need Python for structured checks and later GenAI developer work. Counseling decides the order. After 10th or 12th anyone can apply; school learners of any grade start with counseling first.

  • Where is generative AI practice heading?

    Models write more fluently and structured outputs are more common, but missing context still leads to invented answers. Designing eval cases and refuse rules stays valuable either way.

  • How does CEC help you practise generative AI?

    Tanvi Darji teaches data science, machine learning and artificial intelligence here. You write the job, pack context, demand structured fields and finish an eval score sheet — about 80% practical. We stay with you until you get a job, based on your performance in training, projects and interviews.

  • Who is this path for?

    College students, career changers and working professionals who want practical generative AI workflows. If foundations are still new, counseling may place prompt practice or Python first.

  • How do I reach the Maninagar lab?

    CEC Maninagar is ~2 minutes from Maninagar Railway Station, on the 2nd floor of Gopal Tower. BRTS, AMTS both serve the area. Learners also come from Kankaria, Isanpur, Ghodasar, Khokhra, Meghaninagar, Danilimda. Call +91 75740 10176 before you visit.

  • Do I need to visit before classroom admission?

    For classroom admission we warmly welcome you and your parents to visit — meet mentors and staff, see the lab and ask questions face to face. Call or WhatsApp +91 75740 10176 to book a time. Please choose whichever centre is easier for you if Nikol or Vatva suits your commute better. Learning from outside Ahmedabad? We guide you on a counseling call and complete admission online.

Bring one document task — we will write five eval cases first

In counseling at CEC Maninagar we check your starting point, sketch context and structure rules, and map generative AI beside prompt practice or GenAI developer paths.

Book counseling

Or reach us directly

+91 75740 10176 · info@cecyours.org