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Prompt and Context Engineering: Effective AI Communication

Level 1 Certification Exam - L1:P0-PTE

Exam Overview

L1:P0-PTE

The exam is divided into thematic sections, with questions building from basics to advanced applications.

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Exam Content

Topics covered in this exam

1
Fundamentals

  • Primary goals and differences between prompt engineering (crafting instructions for desired outputs) and context engineering (providing relevant facts/documents for grounding)
  • How LLMs work (as prediction engines guessing next tokens, not human-like understanding)
  • Common failure modes (e.g., hallucinations from missing info, messy outputs from vague instructions)
  • Recommended workflows (prompt first, then ground with context; feeding tool outputs back in agentic flows)
  • Viewing LLMs as sophisticated autocomplete systems

2
Configuration

  • Settings like temperature (controls randomness/creativity; low for consistency, high for diversity), top-K (limits to top likely tokens), top-P (nucleus sampling until probability threshold), output/token limits (affects length and cost)
  • Starting points for configs (conservative: low temp/top-P/K; balanced; creative: higher values)
  • Appropriate uses (e.g., temp 0 for math, higher for writing)

3
Prompting Techniques

  • Basic: zero-shot (direct question), few-shot (3–5 examples for patterns), one-shot
  • Advanced: role prompting (assign persona), system prompting (behavior guidelines), Chain of Thought (CoT; "think step by step"), Self-Consistency (multiple paths, pick common answer), Step-Back (general question first), ReAct (reasoning + actions/tools), Tree of Thoughts (ToT; branching for complex decisions)
  • Tips like using "Let's think step by step" with temp=0 for consistency

4
Best Practices & Pitfalls

  • Best practices: specific/action verbs, positive instructions, structured formats (e.g., JSON), variables for reusability, break complex tasks into chains, provide context from prior interactions, optimize token use
  • Pitfalls: vague/ambiguous instructions (unpredictable outputs), overloading with constraints, negative constraints over positives, over-relying on tools without reasoning, excessive details/deadlines
  • Examples of strong vs. weak prompts for analysis, coding, essays

5
Testing and Evaluation

  • Frameworks: record prompt versions/goals/settings/quality notes
  • A/B testing (compare versions), metrics (accuracy, relevance, completeness, style, format, consistency across runs)

6
Advanced Techniques and Tips

  • Context management in long conversations (summarize past, break tasks)
  • Prompt chaining (sequential steps), structured outputs (JSON for analysis)
  • Multi-modal prompting (explicit about image details)

7
Mixture-of-Experts (MoE) & Prompting

  • MoE architecture: gating network/router selects experts (sub-networks), sparse activation for efficiency/scalability
  • Impact on prompting: domain-specific signals upfront, separate mixed tasks, front-load cues, reduce temp for consistency
  • Benefits/drawbacks: efficiency/specialization vs. routing instability/memory overhead
  • Expert-aware prompting: explicit language/style, avoid vagueness

8
Practical Application

  • Elements for effective prompts in content creation (topic, audience, tone, format), code generation (language, requirements, examples), customer feedback analysis (sentiment, themes, recommendations)

9
Context Engineering Integration

  • RAG (Retrieval-Augmented Generation): prioritize relevant/newest passages, optimize chunking/ranking/deduping/token budgets
  • Combining with prompts: prompts for behavior, context for knowledge

10
Comprehensive/Applied Scenarios

  • Consolidated examples: optimized prompts for reports, Q&A bots, stories; addressing pitfalls in MoE (e.g., front-loading, low temp)

11
Context Engineering

  • Basics: LLM as CPU, context window as RAM; for building AI agents/apps
  • Differences from prompt engineering (more comprehensive, code-like for agents)
  • Agent components: model, tools (external interaction), memory (dynamic history), knowledge (static info), orchestration, guardrails (behavior/safety), audio/speech
  • Multi-agent systems: splitting tasks (e.g., search + summarize), sharing context
  • Strategies: writing/selecting/compressing/isolating context, actions as decisions
  • Advanced: architecture (state management, compression like hierarchical summarization), optimization (accuracy/latency/cost/recovery), security (isolation), memory hierarchies, RAG integration (semantic chunking), temporal reasoning (graph-based), enterprise trade-offs (modular components)

12
6-Step Framework

  • Steps: strong command verbs (e.g., "analyze"), rule of three for context (who/what/when? or past/present/future), logic (reasoning/output format), roleplay (specify expertise), questions (iterative refinement until repetition), voice memos (natural follow-ups)
  • When to use extensive/minimal context (complex vs. simple tasks)
  • Great vs. weak prompters (use questions to refine)
  • Common mistakes: vague commands, generic info
  • Benefits: transforms interactions into expert consultations

13
AI Image Generation Prompting (Nano Banana)

  • Core principles: specificity over generality, visual hierarchy (subject → environment → lighting → technical details), professional photography language (camera terminology, lighting setups, photography styles)
  • Anatomy of effective image prompts: subject description, pose/action, environment, lighting, style, technical specifications, mood/atmosphere
  • Professional photography terminology: lens specifications (85mm, 50mm, 35mm), aperture settings (f/1.4, f/2.8, f/8), depth of field effects (shallow for subject isolation, deep for environmental context), lighting patterns (Rembrandt, studio, natural window light)
  • Best practices: anchor subject with "of the uploaded photo" for consistency, control scene with environment cues, specify style and mood (cinematic, corporate, fine art), borrow photography language for realism, add branding elements (text, logos, graphics)
  • Style categories: corporate/professional (executive portraits, headshots, LinkedIn profiles), creative/artistic (fine art, black and white, documentary), fashion/editorial (magazine style, high fashion, editorial shoots), lifestyle/personal branding (environmental context, authentic moments)
  • Common mistakes: being too vague, conflicting styles, overcomplicating prompts, missing key elements (lighting, pose, environment)
  • Quality control checklist: subject clearly described, pose/positioning specific, lighting type and direction specified, environment/background detailed, camera/technical specs included, mood and style communicated, professional photography language used

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