Understanding Prompt Engineering Basics
📌 Prompt engineering is defined as both an art and a science, involving the iterative development of task-specific instructions for Large Language Models (LLMs).
⚙️ The process is iterative: Idea $\rightarrow$ Prompt Design $\rightarrow$ Model Execution $\rightarrow$ Testing/Feedback $\rightarrow$ Refinement.
🧪 The science lies in the underlying generative models and architectures, while the art involves the creativity in conceiving and crafting the initial prompt.

Key Components of a Prompt
🧱 A good prompt is constituted by two major parts: parameters and structure.
📊 Key parameters include Temperature (controls randomness/creativity, set to 0 for factual tasks, 0.7-0.8 for creative tasks) and Top P (controls the nucleus probability for output selection).
💡 A good prompt structure includes Context (additional information/role), Instruction (specific task), Input Data, and Output Indicator (desired format like CSV or table).

Prompt Design Checklist and Patterns
✅ A checklist for effective prompts includes defining the Goal, detailing the Format, creating a Role (persona), clarifying the Audience, providing ample Context, giving Examples, specifying Style (formal/informal), and defining the Scope (restrictions like Max Length).
🎭 Common prompt patterns include Persona Patterns (e.g., "Act as an analyst"), Audience Persona Patterns (explaining assuming a target audience), and the Template Pattern (providing placeholders for structured output).

Advanced Prompt Strategies and Common Errors
➡️ Zero-shot prompting involves instructing the model directly without providing any examples (e.g., asking for classification without examples).
🎓 Few-shot prompting teaches the model by providing 1-2 examples to guide the desired output style or format.
🧠 Chain of Thought (CoT) is best for logical tasks, providing examples of the reasoning process (e.g., sequential problem-solving steps) to guide complex answers.
🛑 Common errors include writing vague/ambiguous prompts, providing biased examples, or having complex/confusing instructions that overwhelm the model.

Applications and Iterative Refinement
✍️ Prompt engineering applications span Content Generation, Customer Support, Data Analysis/Science (code generation, statistical tests), Research, and even specialized fields like Healthcare and Manufacturing.
🛠️ Proficiency requires constant practice, experimentation, analyzing outcomes, and iteratively refining prompts based on the checklist and feedback given to the model.

Key Points & Insights
➡️ Master the four components of a good prompt: Context, Instruction, Input Data, and Output Indicator to enable better model results.
➡️ Adjust the Temperature parameter carefully; use low values (near 0) for high-stakes factual tasks like code generation to minimize creativity.
➡️ Utilize Persona Patterns by assigning the AI a specific role (e.g., "Act as an analyst") within the context to guide its perspective and output quality.
➡️ For logical problems, employ the Chain of Thought (CoT) strategy, showing the AI the step-by-step reasoning process through examples.

📸 Video summarized with SummaryTube.com on Dec 11, 2025, 13:57 UTC