Video summary

Prompt Engineering Full Course | From Beginner to Pro

Main summary

Key takeaways

Educational

Main ideas / concepts taught

  • Prompt engineering is necessary to use AI tools effectively

    • AI tools can help with work (images, video, audio, code, etc.), but you must know how to prompt correctly.
    • You may need to demonstrate eligibility/access first (the speaker mentions: “you have to give the exam” before using tools).
    • The course targets:
      • Working professionals, freshers, and college students (practical real-life use cases)
      • People who want to crack prompt-engineering-related interviews

  • Understanding the “technology inside the machine”

    • Prompts work best when you understand how the model processes inputs (tone, context, structure), not just superficial “rules”.
    • Analogy: how you speak to a strict vs. friendly boss depends on understanding the other person—similarly, you must understand the model’s internal behavior.

  • Discriminative vs. Generative AI

    • Discriminative AI: focuses on classification
      • Examples: spam vs not spam, face unlock (yours vs not), Netflix genre recommendations
    • Generative AI: focuses on creating new data (text/images/code), where prompting is crucial

  • Why transformers matter for modern LLM prompting

    • Evolution mentioned: RNN → LSTM → Transformer
    • Issues with RNN:
      • weak memory / “vanishing gradient problem”
    • LSTM improvement:
      • “remember vs forget” via gates
    • Transformer (2017):
      • introduces attention, letting the model consider the whole context simultaneously
    • Implication:
      • prompts should be clear and well-structured because transformers leverage context effectively

  • Core LLM prompting fundamentals

    • Prompts (inputs) are processed as tokens, which the model converts to numbers.
    • Tokens cost money for providers (illustrated with “please/thank you” style examples).
    • A context window limits how much text the model can reliably “keep in mind”
      • Analogy: like writing on a blackboard—new writing pushes old writing away
      • If you exceed the context window, the model may forget earlier instructions
        • Example: an initial “reply in Hindi” instruction gets dropped, and output becomes English
    • Approximate context capacity comparisons mentioned:
      • ChatGPT: often up to ~128k tokens (speaker’s stated range)
      • Gemini: up to ~256k (speaker’s stated range)
      • Mentions very large contexts like 1–2M in some setups
      • Other tools (Cloud/Perplexity/Anthropic) have smaller token allowances

  • Types of prompts

    • Direct prompting
      • simple instruction with minimal required context
    • Structure prompting
      • explicit persona/role, context, task, constraints, and output format

  • Reasoning/prompting techniques to improve correctness

    • Zero-shot prompting
      • no examples provided
    • Few-shot prompting
      • provide 2–3 examples in the desired output format
    • Chain-of-Thought (CoT)
      • instruct/encourage step-by-step reasoning to reduce hallucinations for logical problems
      • includes an example where direct emoji generation goes wrong (seahorse), and suggests reasoning can improve complex outcomes
    • Instruction prompting
      • define role/persona + constraints/boundaries + specificity (what to do / not do)
    • Advanced frameworks
      • ReAct (Reason + Act)
        • model loops through thought/action/observation (e.g., searching/browsing) to solve tasks
      • Tree of Thoughts
        • generate multiple candidate strategies (“experts”), critique them, then combine/select the best plan
      • Directional stimulus prompting (hint-based)
        • provide keywords/directions to focus summarization or output
      • Iterative prompting
        • draft → test → revise → repeat until output quality meets requirements

  • Practical workflow for data/business tasks

    • Data extraction prompts
      • act as a data processing expert to extract fields from text into structured JSON
    • Code improvement prompts
      • refactor code with specific requirements (e.g., switch statement, lookup object)
    • Retrieval-Augmented Generation (RAG)
      • LLM alone may not know private/internal knowledge
      • RAG uses:
        • a vector database (data converted into vectors)
        • retrieval of relevant passages
        • augmentation to answer using retrieved context
      • Used for:
        • exam-specific questions
        • summarizing large PDFs
        • tailoring resumes to ATS
        • and more

  • Using AI tools in the course

    • The speaker demonstrates/mentions using multiple tools for different tasks:
      • ChatGPT, Gemini, NotebookLM, Perplexity
      • plus general “LLMs” and references to OpenAI and GitHub in the introduction
    • The course includes practice across these tools, not just theory

Methodology / instruction lists (detailed bullet format)

1) Structure prompting template (persona + context + task + constraints + output)

  • Role / Persona
    • “Act as …” (e.g., senior content marketer with X years)
  • Context
    • Explain the situation: product/brand/event, target audience situation, and why the task matters
  • Task
    • Specify exactly what to produce (e.g., blog post title + 300 words + markdown)
  • Negative constraints / what not to do
    • Avoid jargon, avoid forbidden topics, avoid missing required details
  • Output format / structure
    • Provide format requirements (e.g., H1 header, markdown, word limit)
    • Provide delivery requirements (e.g., table + graphs, JSON array, etc.)

2) Zero-shot prompting procedure

  • Give only the instruction
    • Example category from subtitles: sentiment classification of a review
  • Do not provide examples
  • Accept that the model answers based on its internal knowledge
  • Review output for correctness and adjust if needed

3) Few-shot prompting procedure

  • Provide 2–3 labeled examples
  • Ensure examples match the exact output format
    • Example pattern:
      • Example 1 → sentiment label
      • Example 2 → sentiment label
      • Example 3 → sentiment label
  • Then provide the new input
  • Expect output in the same structured format (e.g., JSON key/value fields)

4) Chain-of-Thought (CoT) prompting concept

  • For complex reasoning tasks:
    • Ask for step-by-step thinking
    • Use reasoning to reduce hallucinations
  • Practical focus:
    • Works best for logical/maths problems
  • Comparison:
    • direct answers may fail
    • step-by-step reasoning can improve correctness

5) Instruction prompting template (role + constraints + boundaries + practical instruction)

  • Role
    • “Act as a senior Python developer…” / “Act as an ATS system…”
  • Constraints
    • What to avoid (e.g., no external libraries)
    • Size limits (e.g., under 20 lines)
  • Specific instructions
    • Additional clarity: audience, tone, coding rules, formatting, etc.

6) ReAct framework prompting steps (Thought → Action → Observation loop)

  • Provide a task that may require research/browsing
  • Instruct the model to:
    • Think
    • Act (perform actions like searching)
    • Observe (use results from the action)
    • Repeat until sufficient information is gathered
  • Final output should be derived from the looped observations

7) Tree of Thoughts prompting steps (multiple candidates → critique → best plan)

  • Ask for 3–4 different “experts”/strategies
  • Add a critique step:
    • evaluate each strategy for risks/costs/weaknesses
  • Final step:
    • combine best parts into a master plan
  • Useful for selecting between multiple approaches (e.g., marketing strategies under budget constraints)

8) Directional stimulus prompting (hint/keyword focus)

  • Provide:
    • Main instruction (e.g., “Summarize the article…”)
    • Keywords / focus points (e.g., subsidies, solar efficiency, employment)
    • Optionally, criteria for what the summary must emphasize
  • Then supply the article text
  • Expect the summary to focus only on the directed concepts

9) Iterative prompting workflow (draft → evaluate → revise loop)

  • Iteration 1
    • generate first version (e.g., image prompt or resume draft)
  • Iteration 2
    • add corrections based on what’s missing (color warmth, details, formatting, etc.)
  • Continue:
    • repeat refining until the desired quality/output is achieved

10) RAG (Retrieval-Augmented Generation) workflow

  • Prepare your reference material (e.g., PDF notes, internal documents)
  • During prompting, instruct the system to:
    • Retrieve relevant passages (via vector search)
    • Use those passages to ground the answer
    • Produce structured outputs like summaries, exam-specific questions, tailored resumes, etc.

Speakers / sources featured (identified)

  • Swati (the course instructor/speaker)
  • Company/tool sources referenced
    • IBM
    • Google
    • Microsoft
  • AI tools / models referenced
    • ChatGPT (OpenAI)
    • Gemini
    • NotebookLM
    • Perplexity
    • Anthropic (mentioned in token/context comparison)
    • OpenAI (mentioned for token cost examples)
  • Research/technical concept referenced
    • “Attention is all you need” / Transformer attention mechanism (referenced as the Transformer paper idea)
  • Other sources/tools referenced
    • Wikipedia (used as an example article source for summarization)

Original video