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Generative AI & Prompt Engineering Internship

Learn to get reliable, useful results from AI models: prompt design, evaluation, and building small AI tools that solve real tasks instead of chatting for fun.

ChatGPT / Claude / GeminiPythonOpenAI or Anthropic APIPrompt evaluation

What you'll work on

Full syllabus: all 60 graded tasks

These are the actual tasks the internship portal assigns for this domain. You submit each one with proof (a GitHub link or document) and a mentor reviews it.

Level 1: Foundations (22 tasks)
  1. Compare three AI assistants. Ask the same 5 questions (coding, maths, summary, translation, a fact) to three free AI assistants. Record answers in a table and note which was right, wrong or vague.
  2. Write a prompt-engineering cheat sheet. Document role, task, context, format and constraints with one before/after example each. Commit it as README.md in a repo named genai-internship.
  3. Zero-shot vs few-shot prompting. Classify 20 product reviews as positive/negative with a zero-shot prompt, then with 3 examples. Report accuracy for both and explain the difference.
  4. Structured output with JSON. Write a prompt that turns 10 messy job postings into JSON (title, company, skills[], salary). Validate the JSON with a short Python or JS script.
  5. Spot hallucinations. Ask an AI 10 questions about your college or city. Verify each answer from a real source and list which facts were invented. Write 3 rules to reduce this.
  6. Prompt for a study plan. Build a reusable prompt that creates a 4-week study plan for any subject and level. Show outputs for two different subjects.
  7. Summarise a long article. Summarise a 2,000+ word article in 3 lengths (tweet, paragraph, one page). Check every summary against the source for errors.
  8. Call an LLM API. Use any free LLM API from Python or Node.js to send a prompt and print the reply. Keep the API key in an environment variable, never in the code.
  9. Token and cost calculator. Write a script that estimates tokens for a text and the cost for 1,000 requests at a given price per million tokens.
  10. AI image prompt gallery. Create 6 images with a free image generator for one theme. Record each prompt and what you changed between versions.
  11. Responsible AI checklist. Write a one-page checklist covering privacy, bias, copyright and disclosure for using AI at work, with a real example for each point.
  12. Chain-of-thought for reasoning. Give an AI 10 word problems with and without asking it to reason step by step. Compare correctness and explain when it helps.
  13. Prompt library in Notion or Markdown. Collect 15 tested prompts for study, coding and career tasks, each with the output it produced and notes.
  14. Compare model settings. Run the same prompt at 3 temperatures and 2 max-token limits and describe how the output changes.
  15. AI for coding, used honestly. Use an AI assistant to explain, test and refactor your own code; record what it got wrong and how you checked.
  16. Persona prompts. Write prompts for 4 personas (tutor, interviewer, reviewer, translator) and test each with 3 inputs.
  17. Detect AI mistakes in maths and code. Give an AI 10 tricky problems, verify every answer yourself and categorise the mistakes.
  18. AI policy for your college project. Write rules for using AI in a team project: allowed uses, disclosure and checking outputs.
  19. Prompt patterns. Practise 6 patterns (role, few-shot, chain of thought, critique-and-revise, template, delimiters) with examples.
  20. Multimodal prompts. Use a model that accepts images to describe charts and screenshots; check every claim.
  21. AI for learning. Use AI to create flashcards and practice questions for one subject; verify answers from the textbook.
  22. Cost comparison. Compare prices of 3 model sizes for a task and estimate monthly cost for 10,000 users.
Level 2: Building real features (20 tasks)
  1. Build a CLI chatbot with memory. A terminal chatbot that keeps the conversation history, has a system prompt and a /reset command. Push it with a README and sample transcript.
  2. Prompt templates with variables. Build a small library of 5 prompt templates (email, summary, quiz, code review, translation) filled from variables, with tests for each.
  3. Extract data from PDFs. Extract invoice number, date, items and total from 5 sample PDF invoices using an LLM, saving the results as CSV. Report any field it got wrong.
  4. Build a quiz generator. Given a topic and difficulty, generate 10 MCQs with answers and explanations as JSON, then show them in a simple web page.
  5. Function calling / tool use. Let an LLM call 2 functions you write (weather lookup with mock data, calculator). Log every tool call and result.
  6. Evaluate prompts with a test set. Create 30 test inputs with expected outputs for one task. Score 3 prompt versions automatically and pick the best with numbers.
  7. Content moderation filter. Classify 50 sample comments into safe/spam/abusive with an LLM. Measure precision and recall against your own labels.
  8. Streamed responses in a web app. Build a small web page that streams an LLM answer token by token from your backend instead of waiting for the full reply.
  9. Resume feedback tool. Upload a resume (text or PDF) and get structured feedback: strengths, gaps and 5 rewritten bullet points. Remove personal data before sending.
  10. Multilingual assistant. Answer the same question in English, Tamil and Hindi. Have a native speaker rate the quality and record the scores.
  11. Mock interview bot. Build a bot that asks role-specific interview questions one at a time and scores answers with feedback.
  12. Document Q&A with citations. Answer questions about a PDF with page citations; show 5 correct answers and 2 refusals.
  13. Batch processing with an LLM. Process 100 rows of text (classify or summarise) with retries, rate limits and a progress log.
  14. Image generation workflow. Create a consistent set of 6 images for a brand using a style guide prompt and document the iterations.
  15. Speech to text to summary. Transcribe a short talk with a speech model and summarise it into notes and action points.
  16. Email reply assistant. Draft replies to support emails with tone options and a human approval step.
  17. Code review bot. Review a pull request diff and comment on bugs and style; measure how many comments are correct.
  18. Translation quality check. Translate 20 sentences into Tamil or Hindi and back; score meaning preservation.
  19. Structured data extraction at scale. Extract fields from 200 short documents with schema validation and an error report.
  20. Content moderation pipeline. Combine rules and an LLM to flag harmful content, with logging and appeal handling.
Level 3: Professional work and capstone (18 tasks)
  1. Retrieval-augmented Q&A over your notes. Index 20+ pages of your own notes with embeddings, retrieve the top chunks and answer questions with citations. Show 10 questions it answers correctly.
  2. College helpdesk bot. A bot that answers admission, fees and timetable questions only from a provided document and says 'I don't know' otherwise. Deploy it publicly.
  3. Guardrails and prompt-injection tests. Write 15 injection attempts against your bot (e.g. 'ignore your instructions'). Add defences and show the before/after results.
  4. Cost and latency dashboard. Log tokens, cost and response time for every request of your app and show daily totals in a simple dashboard.
  5. Fine-tune or few-shot a classifier. Build a support-ticket classifier (billing, technical, other) and compare prompting vs a small fine-tuned or embedding-based model on 100 tickets.
  6. AI meeting-notes app. Upload a meeting transcript and produce a summary, decisions, action items with owners and dates, exported as a PDF or Markdown file.
  7. Capstone: deploy a GenAI product. Ship one complete GenAI app with sign-in, rate limiting, error handling and a privacy note. Submit the live URL, repo and a 3-minute demo video.
  8. Write a case study. Publish a 1,000-word case study of your capstone: problem, prompts, evaluation numbers, costs and what you would do next. Share it on LinkedIn.
  9. Content assistant for a small business. Generate weekly social posts from a product list with a human review step before posting.
  10. Evaluate with an LLM judge. Score your app's answers with a rubric-based LLM judge and compare against your own ratings on 20 items.
  11. Prompt versioning. Store prompts in code with versions and tests so a prompt change that breaks outputs fails CI.
  12. Privacy-safe AI app. Add PII detection and masking before sending text to a model, with tests for names, phones and emails.
  13. Demo day presentation. Present your best GenAI project in 5 minutes: problem, demo, numbers, risks and next steps.
  14. Fine-tuning vs prompting study. For one task, compare prompting, few-shot and a fine-tuned small model on quality and cost.
  15. Voice assistant prototype. Speech in, LLM answer, speech out, with latency measured at each step.
  16. Guarded customer FAQ bot. Answer only from approved FAQs, escalate everything else, log every conversation.
  17. Bias and safety testing. Test your app with inputs about gender, region and language; document and reduce unfair outputs.
  18. GenAI product launch plan. Write a launch plan: users, pricing, risks, monitoring, support and success metrics.

Real software work, not just training

Every Generative AI & Prompt Engineering task is reviewed by our team and saved to your portfolio. Interns who pass the readiness check can take real work from Training Trains, W3AppDevelopers and DomainHostly projects on the Company Work board.

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Frequently asked questions

Do I need experience to join the Generative AI & Prompt Engineering internship?+

Basic computer skills are enough to start; Python helps for the later API tasks and is taught as you go.

What will I do in the Generative AI & Prompt Engineering internship?+

You complete 60 graded tasks in three levels: foundations, real features, and professional work. Each task is submitted with GitHub or document proof and reviewed. The final capstone is: Capstone: deploy a GenAI product.

Do I get a certificate for the Generative AI & Prompt Engineering internship?+

Yes. After your required tasks are approved, your attendance is above 75% and you pass the proctored final exam, you get a graded completion certificate that anyone can verify at trainingtrains.com/verify.

Can I do the Generative AI & Prompt Engineering internship online?+

Yes. You can do it fully online from home, or offline at our Erode and Perundurai branches. Tasks, quizzes and mentor reviews are the same in both modes.

Available both online (work from home) and offline at our Erode and Perundurai branches - see the full internship program details for departments, duration, and the application process.

Apply for the Generative AI & Prompt Engineering internship