Prompt Engineering
The Prompt Engineering course teaches practical techniques for designing effective prompts, from foundational concepts to advanced patterns, enabling learners to generate reliable, structured outputs while ensuring safety and security.
Who Should Take This
It is ideal for data scientists, product managers, and AI developers who regularly work with large language models and want to improve prompt reliability. Participants should have basic familiarity with LLMs and aim to apply systematic prompting patterns to reduce trial‑and‑error, meet output specifications, and mitigate safety risks.
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
61 learning goals
1
Prompt Engineering Foundations
6 topics
Describe how large language models process prompts including tokenization, context windows, attention over input tokens, and how prompt structure influences the probability distribution over output tokens
Describe prompt components including system instructions, user messages, assistant prefills, examples, and constraints and explain how each component shapes model behavior
Describe generation parameters including temperature, top-p, top-k, frequency penalty, presence penalty, and stop sequences and explain how each affects output diversity and quality
Apply the distinction between instructional and conversational prompting styles and explain when each is more effective based on task type, model capability, and desired output format
Analyze how context window limitations affect prompt design including the trade-offs between instruction length, example count, and available space for generation in constrained contexts
Describe model capabilities and limitations including reasoning depth, factual accuracy boundaries, instruction following reliability, and how understanding model behavior informs effective prompt design
2
Core Prompting Techniques
8 topics
Apply zero-shot prompting including clear task specification, output format definition, and constraint articulation for tasks where the model has sufficient pretrained knowledge
Apply few-shot prompting including example selection strategies, example ordering effects, example format consistency, and how demonstrations calibrate model behavior for specific tasks
Apply chain-of-thought prompting including step-by-step reasoning elicitation, explicit reasoning chains, and how intermediate reasoning steps improve accuracy on complex multi-step problems
Apply self-consistency techniques including sampling multiple reasoning paths, majority voting, and how aggregating diverse chain-of-thought responses improves answer reliability
Apply role and persona prompting including expert role assignment, domain-specific personas, and how framing the model's identity influences response style, depth, and domain accuracy
Analyze the effectiveness of different prompting techniques across task types and model sizes and evaluate when simple prompts outperform elaborate prompt engineering
Apply constraint and negative prompting including specifying what the model should not do, boundary conditions, and how explicit constraints reduce unwanted behaviors and hallucinations
Apply output formatting instructions including markdown, XML, numbered lists, and how explicit formatting directives improve consistency and parseability of model-generated content
3
Advanced Prompting Techniques
7 topics
Apply tree-of-thoughts prompting including branching exploration, self-evaluation of reasoning paths, and backtracking for problems requiring search through a solution space
Apply prompt chaining including decomposing complex tasks into sequential prompt calls, passing intermediate results between stages, and designing robust chain architectures
Apply meta-prompting including prompts that generate prompts, self-refinement loops where the model critiques and improves its own output, and iterative prompt optimization
Apply retrieval-augmented prompting including context injection patterns, citation requirements, grounding instructions, and how to prompt models to distinguish between provided context and parametric knowledge
Analyze when advanced prompting techniques provide genuine improvement versus unnecessary complexity and evaluate the cost-benefit trade-off of multi-step prompt architectures
Apply reflection and critique prompting including asking the model to evaluate its own output, identify potential errors, and generate improved versions through self-assessment loops
Apply multi-agent prompting including simulating debate between multiple perspectives, using critic and generator roles, and how adversarial prompting improves output quality through dialectic refinement
4
Structured Output Generation
6 topics
Apply JSON and structured data extraction prompting including schema specification, field-by-field extraction, nested object handling, and validation of model-generated structured outputs
Apply code generation prompting including specification clarity, language and framework targeting, test case inclusion, and how to structure prompts that produce executable, correct code
Apply classification and labeling prompts including taxonomy definition, confidence calibration, multi-label handling, and chain-of-thought classification for nuanced categorization tasks
Analyze structured output reliability including format compliance rates, common failure patterns, repair strategies for malformed outputs, and when to use constrained generation versus free generation
Apply table and spreadsheet generation including formatting structured tabular data, CSV output, comparison tables, and how to prompt for consistent column alignment and data formatting
Apply function and tool use prompting including defining tool schemas, instructing models when and how to call tools, and how tool-use prompting extends model capabilities beyond text generation
5
Prompt Safety and Security
5 topics
Describe prompt injection attacks including direct injection, indirect injection via retrieved content, and jailbreaking techniques and explain the security implications for LLM applications
Apply defensive prompt design including input validation instructions, output boundary enforcement, privilege separation between system and user content, and canary token monitoring
Apply content safety prompting including harmful content refusal instructions, sensitive topic handling guidelines, and how to balance helpfulness with safety in system prompt design
Analyze the arms race between prompt attacks and defenses and evaluate multi-layer defense strategies including input filtering, system prompt hardening, and output monitoring
Apply data privacy in prompting including avoiding PII in prompts, data minimization principles, and how to design prompt workflows that protect sensitive information from model providers
6
Prompt Evaluation and Testing
6 topics
Describe prompt evaluation challenges including subjectivity of quality assessment, task-specific metrics, and why single-example testing is insufficient for production prompt deployment
Apply prompt testing methodologies including test suite design, edge case identification, regression testing across model versions, and systematic prompt comparison experiments
Apply automated prompt evaluation including LLM-as-judge scoring, rubric-based assessment, reference comparison, and statistical analysis of prompt performance across diverse inputs
Analyze prompt evaluation strategy including when human evaluation is necessary, how to design annotation guidelines, inter-rater reliability measurement, and cost-effective evaluation pipelines
Apply prompt debugging techniques including identifying why prompts fail, common failure patterns, systematic troubleshooting workflows, and iterative refinement based on error analysis
Apply cost-aware prompt optimization including minimizing token usage, balancing quality with cost, and how to design prompts that achieve acceptable results with smaller or cheaper models
7
Domain-Specific Prompting
8 topics
Apply summarization prompting including abstractive and extractive instructions, length control, key point extraction, and multi-document summarization strategies
Apply writing and content creation prompting including tone control, audience targeting, style consistency, iterative refinement, and how to maintain voice across long-form generated content
Apply analysis and reasoning prompts including data interpretation, argument evaluation, comparative analysis, and how to structure prompts that produce rigorous analytical outputs
Apply education and tutoring prompts including Socratic questioning, adaptive difficulty, misconception identification, and scaffolded learning through progressively complex prompt interactions
Analyze domain-specific prompt design patterns including how domain constraints, terminology, and output requirements differ across industries and task categories
Apply data extraction and transformation prompting including parsing unstructured text into structured data, entity extraction from documents, and batch processing patterns for data cleaning tasks
Apply research and synthesis prompting including literature review assistance, multi-source comparison, evidence-based reasoning, and how to structure prompts that produce balanced analytical outputs
Apply translation and localization prompting including preserving tone across languages, cultural adaptation, terminology consistency, and quality evaluation for translated content
8
Multimodal Prompting
4 topics
Apply image analysis prompting including describing visual content expectations, spatial reasoning instructions, and how to combine text and image inputs for accurate visual question answering
Apply document and table extraction prompting including OCR correction guidance, structured data extraction from images, and multi-page document comprehension strategies
Analyze multimodal prompting limitations including hallucination patterns in visual grounding, spatial reasoning failures, and strategies for improving reliability of vision-language model outputs
Apply audio prompting patterns including transcription guidance, audio analysis instructions, and how to structure prompts for speech understanding and audio content analysis tasks
9
Prompt System Design
6 topics
Apply system prompt architecture including layered instruction design, priority ordering of directives, exception handling instructions, and modular prompt component reuse
Apply prompt versioning and management including change tracking, A/B testing prompt variants, prompt template systems, and configuration management for production prompt deployments
Apply prompt optimization techniques including iterative refinement based on failure analysis, token efficiency improvements, and systematic ablation testing to identify critical prompt components
Analyze prompt engineering as a software engineering discipline including maintainability, documentation, team collaboration, and how to build organizational prompt engineering competency
Apply prompt libraries and reuse patterns including creating organization-wide prompt templates, sharing effective prompts across teams, and building institutional knowledge around prompt engineering
Analyze the future of prompt engineering including how model improvements may reduce prompting complexity, the shift from manual to automated prompt optimization, and the evolving role of prompt engineers
10
Cross-Model Prompting
5 topics
Describe how prompting strategies vary across model families including instruction-following fidelity, reasoning capabilities, context utilization patterns, and output format compliance differences
Apply model-aware prompt adaptation including adjusting complexity for smaller models, leveraging extended context for capable models, and designing prompts that degrade gracefully across model tiers
Analyze the relationship between model capability and prompt complexity including when models benefit from explicit instructions versus when they perform better with minimal guidance
Apply open-source model prompting including adjusting for different instruction formats, handling models with limited instruction following, and how fine-tuned versus base models require different prompt approaches
Analyze prompt portability across models including why prompts optimized for one model may fail on another, strategies for building model-agnostic prompts, and managing prompt migrations during model upgrades
Hands-On Labs
15 labs
~360 min total
Console Simulator
Code Sandbox
Practice in a simulated cloud console or Python code sandbox — no account needed. Each lab runs entirely in your browser.
Scope
Included Topics
- Prompt fundamentals (tokenization, context windows, generation parameters), core techniques (zero-shot, few-shot, chain-of-thought, self-consistency, role prompting), advanced techniques (tree-of-thoughts, prompt chaining, meta-prompting, RAG prompting), structured output generation (JSON, code, classification), prompt safety and injection defense, evaluation and testing, domain-specific prompting (summarization, writing, analysis, education), multimodal prompting, system prompt architecture, cross-model adaptation
Not Covered
- LLM training and pretraining details (covered in Deep Learning and NLP domains)
- Specific API implementation code for providers
- Fine-tuning and RLHF (covered in LLM App Dev and RL domains)
- Natural language processing theory beyond practical prompting applications
- UI/UX design for AI-powered interfaces
Ready to master Prompt Engineering?
Adaptive learning that maps your knowledge and closes your gaps.
Subscribe to Access