SV Infotech · Dilsukhnagar, Hyderabad
Generative & Agentic AI
Personal study sheet
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Set your starting point
Useful foundations: Python foundations, APIs and basic data handling help. Discuss your current programming level before choosing an entry point.
Explore the learning roadmap
01Understand models and their limits
Distinguish language generation from factual retrieval and deterministic processing. Explain tokens, context and probabilistic output before designing a task around a model.
02Design prompts and structured responses
Specify a task, relevant context and a useful output format. Check the returned structure and handle missing or invalid responses instead of assuming every answer follows instructions.
03Ground answers with retrieval
Break documents into useful retrieval units and compare search results with the question. Preserve source references and test whether an answer is supported by the retrieved material.
04Connect tools with controlled permissions
Define what a tool may read or change, validate its arguments and separate trusted instructions from external content. Require review for consequential actions in an agent workflow.
05Evaluate quality and failure cases
Create representative questions, expected checks and difficult examples. Measure groundedness, task completion, latency and cost; inspect failures that a fluent answer can conceal.
06Build a reviewable AI prototype
Combine an interface, model interaction, logging and a clear stopping rule. Document personal-data handling, rate limits and what the system should do when it cannot answer reliably.
Generative & Agentic AI: a worked study example
Illustrative scenario for discussion and self-study.
Give a document-answering assistant three short policy documents and a question whose answer is absent. A useful result should state the evidence gap rather than invent a policy. For an answer that is present, require a relevant source reference and test whether that source actually supports the statement.
What to check in your work
- Include answerable and unanswerable questions in the evaluation set.
- Require review before an assistant performs a consequential action.
How this pathway compares
Prompting, retrieval and agent execution solve different problems. Python helps you build and inspect the application; the AI Agents guide looks more closely at state, tools and controlled execution.
Check your reasoning
What should an AI assistant do when its sources disagree?
Suggested explanation
Show the conflicting evidence and its source context rather than silently choosing a convenient answer. Ask for clarification or human review when the decision matters. A confident writing style does not resolve an evidence conflict, and a retrieved source can itself be outdated or wrong.
Plan the conversation
Ask about the actual syllabus, practical environment and feedback arrangements. Confirm fees, duration, trainer and current batch schedule directly; this sheet supplies none of those details.
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