No content

Prompt Engineering Fundamentals Build your intuition for crafting better prompts by iteration & validation. Think art – not science.

What will we be covering in this lesson? Define prompt engineering – what is it and why is it important? Know prompt construction – how are they structured and used? Learn prompting best practices - improve quality with iteration. Apply learned techniques to real examples – in our code challenge

Let’s Review – Terminology We’ve Seen So Far Open AI GPT-3.5-turbo Prompt: I “instruct” the AI what to do in natural language (text, chat) A type of AI that generates new content, using LLMs, in response to a user “prompt” An AI model trained on massive datasets and specialized to work on natural language tasks Text input to LLM that “instructs” it what to do – sets model weights and influences response Completion: The AI “predicts” what it thinks I need in response (text, code) What is Generative AI What are Large Language Models What are Prompts Optimized for Chat: 4097 Tokens | Trained to Sep 2021

Let’s Review – Our Application Context We’re an Education Startup building AI apps for Personalized Learning Collaborating to bring AI Innovation To Education – Educator Blog, Jun 2023 Let’s Review – Our Target Application & Audience Administrator I need an assistant that can help me analyze all the curriculum data – and identify gaps in coverage so I can plan better for next year. Educator I need an assistant can help me generate a lesson plan for a specific topic and target audience - and I need this to be presentation-friendly by default. Student I need a tutor that can coach me on this subject in a way that matches my skill level & learning style – I like examples, and I want hints when I get stuck

Lesson Units Introduction Prompt Engineering Definition Prompt Engineering Motivation Prompt Engineering Concepts Prompt Engineering Practices Summary

How does a Prompt work? LLM sees prompt as sequence of tokens … Try it yourself: https://platform.openai.com/tokenizer

How does a Prompt work? Foundation LLMs will simply predict the next token The user did not specify any instructions. AI treats this as request for information and predicts response from trained data. Try it yourself:: https://oai.azure.com/portal/chat

How does a Prompt work? Instruction-tuned LLM extends base behavior for task Add context with a “system” message that tunes the default LLM behavior. Try it yourself:: https://oai.azure.com/portal/chat

Try it yourself:: https://oai.azure.com/portal/chat How does a Prompt work? Instruction-tuned LLM extends base behavior for task Then try the same content snippet again as your user input. Do you see the difference?

Try it yourself:: https://oai.azure.com/portal/chat Definition What is Prompt Engineering? A prompt is the text input given to the Generative AI model. Prompt engineering is the process of designing and optimizing the prompt till the response meets the user’s expectations for relevance or quality

Try it yourself:: https://oai.azure.com/portal/chat Motivation What is Prompt Engineering Necessary? • LLMs are stochastic in nature since prompts “program” the model, the responses are even more sensitive to construction of prompt. • LLMs can hallucinate responses since pre-trained data has a cutoff date, they may predict completions that are not grounded in fact. • LLMs have diverse capabilities every model has unique features and quirks that can be tuned for better quality responses

Try it yourself:: https://oai.azure.com/portal/chat Model Are Stochastic Same prompt Same model Varying response 1st Attempt 2nd Attempt

Try it yourself: https://chat.openai.com/?model=text-davinci-002-render-sha Models Can Hallucinate Prompts give responses that appear valid but may not be true

Try it yourself:: https://chat.openai.com/?model=gpt-4 Model Capabilities Matter Same prompt Newer model Better response

Read the Article: https://github.blog/2023-06-20-how-to-write-better-prompts-for-github-copilot/ Case Study Real-World Usage in GitHub Copilot “For example, when asking GitHub Copilot to draw an ice cream cone using p5.js, a JavaScript library for creative coding, we kept receiving irrelevant suggestions—or sometimes no suggestions at all.” “Draw an ice-cream cone with ice cream using p5.js”

Read the Article: https://github.blog/2023-06-20-how-to-write-better-prompts-for-github-copilot/ Case Study How is Prompt Engineering Used In Real Apps? “When we adjusted our prompt, we were able to generate more accurate results” “3 best practices for prompt crafting - set the stage with high-level goal, be simple & specific, give examples” Draw an ice-cream cone with an ice cream scoop and a cherry on top. The ice cream cone will be a triangle with the point facing down .. The ice cream scoop will be a half circle on top of the cone … …

Lesson Units Introduction Prompt Engineering Definition Prompt Engineering Motivation Prompt Engineering Concepts Prompt Engineering Practices Summary

Simple: Content Type in text See completion In the simplest form, you provide a text input (prompt) and the model predicts the next tokens (completion) This is the fundamental behavior any LLM. Remember it. Try it yourself:: https://oai.azure.com/portal/chat

Complex: Content Multi-turn conversation with context Prompt text as ‘messages’ that carry system context (priming) and user/assistant exchanges (history) as sliding token window Try it yourself: https://platform.openai.com/examples/default-marv-sarcastic-chat

Simple: Instruction Text input specifies task, not content Write a description of the civil war. Try it yourself:: https://oai.azure.com/portal/chat

Complex: Instruction Text input specifies task, adds details Write a description of the civil war. Provide key dates and events and describe their significance. Try it yourself:: https://oai.azure.com/portal/chat

Complex, Formatted: Instruction Text input specifies task, adds details, defines format Write a description of the Civil War in 1 paragraph. Provide 3 bullet points with key dates and their significance. Provide 3 more bullet points with key historical figures and their contributions. Return the output as a JSON file Try it yourself:: https://oai.azure.com/portal/chat

Construct: Instruction + Primary Content Specify task and provide content as context for it Jupiter is the fifth planet from the Sun and the largest in the Solar System. It is a gas giant with a mass one-thousandth that of the Sun, but two-and-a-ha … Summarize this in 2 short sentences Try it yourself:: https://oai.azure.com/portal/chat

Primary Content: Provide Examples Examples as in- context guidance Model reacts to examples, infers response patterns Zero-shot prompting User provides 1 instruction (explicit) + 0 examples Try it yourself:: https://oai.azure.com/portal/chat

Primary Content: Provide Examples Examples as in- context guidance Model reacts to examples, infers response patterns One-shot prompting User provides 0 instructions + 1 example (implicit) Try it yourself:: https://oai.azure.com/portal/chat

Few-shot prompting User provides 0 instructions + few examples (improve inference) Try it yourself:: https://oai.azure.com/portal/chat Primary Content: Provide Examples Examples as in- context guidance Model reacts to examples, infers response patterns

Try it yourself:: https://oai.azure.com/portal/chat Primary Content: Provide Cues Cues ‘prime’ the response Model ‘takes the cue’ to favor completions that match pattern 0 cues

Try it yourself:: https://oai.azure.com/portal/chat Primary Content: Provide Cues Cues ‘prime’ the response Model ‘takes the cue’ to favor completions that match pattern 1 cue

Try it yourself:: https://oai.azure.com/portal/chat Primary Content: Provide Cues Cues ‘prime’ the response Model ‘takes the cue’ to favor completions that match pattern 2 cues

Try it yourself:: https://oai.azure.com/portal/chat Primary Content: Use Templates Pre-Defined “Recipes” for Repeatability Users can “reuse” core templates from an application UI System message templates can set “personality” consistently

Read About It : Prompt Engineering and LLMs with Langchain Primary Content: Use Templates Pre-Defined “Recipes” for Repeatability Apps can customize & use templates with APIs for automation at scale User message templates with placeholders for dynamic input binding

Lesson Units Introduction Prompt Engineering Definition Prompt Engineering Motivation Prompt Engineering Concepts Prompt Engineering Practices Summary

Prompt Engineering Mindset Prompt Engineering is a trial-and-error process that also relies on your intuition. 1. Build domain understanding – use your domain expertise to customize prompt for relevance. 2. Build model understanding – adapt prompt to suit model strengths & weaknesses to improve quality. 3. Iterate & Validate – define acceptance or termination criteria so you iterate to meet expectations but don’t over-engineer the prompt to reduce reusability

Prompt Engineering Best Practices 1. Evaluate the latest models 2. Separate instructions & context 3. Be specific & clear 4. Be descriptive & use examples 5. Use cues - think ‘priming’ responses 6. Double down – think reinforcement 7. Order matters – think recency bias 8. Give the model an out – think fallback

Code Challenge – Build Intuition With Trial & Error Set model and parameters {text} used as a prompt template variable for consistency.& reuse Prompt template example Prompt request made on model Run in container Jupyter Notebook Python 3.10 kernet (Code cell) Actual execution output (Markdown cell) Sample response

What We Learned Today Prompts help you “program” Gen AI What is prompt engineering? Why should we care? How are prompts constructed? How can we optimize prompts? Art, not science Code Challenges

Prompt Engineering Fundamentals Build your intuition for crafting better prompts by iteration & validation. Think art – not science.