Generative AI | IRA Intellix Academy
10-minute course demo
English synthetic narration; captions included.

00:00 — Welcome to IRA

Welcome to IRA Intellix Academy. In this introductory lesson, we explore Generative AI. Build useful AI workflows with reliable references. You will learn the basic ideas, follow a practical example, and try a small task yourself. Keep a notebook nearby. The example uses fictional information for learning. Our goal is to understand the decisions behind the work, so you can explain both what you would do and why.

00:30 — What generative AI does

Generative AI produces outputs such as text, images, or code in response to input and context. It can help draft, transform, explain, and organise information. However, a fluent answer is not proof that the content is correct. A model may produce unsupported statements or misunderstand the task. That is why a useful application needs more than a text box connected to a model. It needs appropriate information, clear boundaries, evaluation, and a plan for uncertainty. In this lesson, we will design an assistant that answers a student's course-schedule question using approved reference information and routes unresolved cases to a person.

01:30 — Choose a bounded use case

Start by defining a narrow task. Our assistant answers questions about published training-batch schedules. It does not invent future batches, guarantee that a seat is available, approve a payment, or change admission rules. Those boundaries make the application easier to evaluate. Identify the authoritative source and the person responsible for maintaining it. Decide what the assistant should do when the source is missing, outdated, or ambiguous. A clear fallback is part of the product, not an embarrassment. Saying that a date is not available and directing the student to the academy can be more useful than delivering a confident but unsupported answer.

02:30 — The student's schedule question

Consider a fictional student asking when the next cloud batch starts. Our teaching dataset contains a published Cloud Foundations batch starting on October twelfth at seven in the evening, India Standard Time. This is invented example data, not IRA's actual schedule. The assistant must identify the relevant course and use an approved current record. If several courses have similar names, it should ask a clarifying question. A student may mean Cloud and DevOps rather than a different cloud programme. Matching words alone is not always enough. The application must connect the question to the correct record before it can offer a reliable answer.

03:30 — Retrieve relevant approved information

The retrieval step finds information relevant to the question. A knowledge-based application may search documents or structured records, but it must also respect access permissions and publication status. A draft schedule should not become a public answer simply because it contains matching words. Check that the selected reference belongs to the right course and is valid for the intended time period. Retain a source identifier so the answer can explain where its information came from. Retrieval can fail or return irrelevant material, so evaluate that step separately. A language model cannot reliably correct every source-selection error after the wrong record has already been supplied.

04:30 — Generate a grounded response

Now provide the selected reference and clear instructions to generate the response. The assistant should answer the student's question using the supplied information, preserve the timezone, and avoid adding unsupported promises. In our fictional example, it can state that the published Cloud Foundations batch starts on October twelfth at seven PM IST. It should not add that registration is guaranteed or that a discount is available unless the approved source says so. Grounding reduces some errors but does not remove the need for checks. Review whether the output matches the reference and whether it communicates the information in a form the student can understand.

05:30 — Handle missing and conflicting evidence

What if no approved schedule is found? The assistant should explain that it cannot confirm the date from the available information and provide the appropriate next step. If two current references disagree, flag the conflict for review. If the timezone is missing and matters to the answer, ask for clarification or refer to the source owner. Do not instruct the system to always produce a date regardless of the evidence. Also treat instructions embedded in retrieved content as untrusted source text, not as authority to change the assistant's rules or reveal information. Reliable applications distinguish between the information they are reading and the instructions they are authorised to follow.

06:30 — Evaluate the complete workflow

Evaluate the whole workflow with representative cases. Include a straightforward question, an ambiguous course name, a missing schedule, an outdated record, and conflicting dates. Check whether retrieval chose the correct source, whether the answer matched it, and whether the fallback worked. A successful example alone is not enough evidence. Record failures so the team can improve the specific step responsible. After deployment, keep references current and monitor unresolved requests. Avoid placing unnecessary personal information into prompts or logs. If the assistant later gains the ability to change records or trigger actions, add explicit controls and confirmation appropriate to those actions instead of treating every generated suggestion as permission to execute it.

07:30 — Your assistant-design exercise

Design a schedule assistant on one page. Write its permitted task, name its authoritative source, and describe which records it may use. Create an answer format that includes the course, date, time, timezone, and reference. Then write a fallback for missing or conflicting information. Pause and test a case where the only matching batch is unpublished. A strong design should not expose that draft as a confirmed public schedule. Finally, list six tests that cover success and failure conditions. You now have the outline of a useful generative AI workflow: retrieve the right evidence, produce a bounded response, verify it, and involve a person when the evidence is insufficient.

08:30 — Why choose IRA?

Why choose IRA Intellix Academy? Start with live, mentor-led learning, guided practice, and practical projects. Use each session to ask questions and understand the reasoning behind a solution. Practise the method yourself, keep your notes, and review your results. Learning Hub recordings let enrolled learners revisit published lessons. After completing your course lessons, career preparation can include mock interviews, resume preparation, and guidance for Naukri and LinkedIn profiles, depending on your selected package. Confirm the current course schedule and package inclusions with the academy before registering. Training supports your preparation; it does not guarantee employment or a particular salary. Bring curiosity, consistent practice, and a willingness to improve.

09:30 — Build your next chapter

You have completed this introductory demo for Generative AI. Useful AI needs context, evaluation and human oversight. Your next step is to explain the example in your own words and complete the practice task. Visit www dot IRA Intellix dot com to explore the course, ask about the current batch, and register. IRA Intellix Academy. Learn a skill. Build your next chapter.