Advanced Level

What it is: Understand prompts not as text, but as a structured context system.

Why: Modern models respond to context, not isolated commands.

Concepts: Context as a contract, a living system, layers of information.

What it is: Organize context into layers with distinct purposes.

Why: Each layer has a specific priority and function.

Concepts: System prompt, global context, task context, data layer.

What it is: How models resolve conflicts between different context levels.

Why: Poorly defined priorities lead to unexpected behavior.

Concepts: Override, inheritance, scope, conflict resolution.

What it is: Distinguish between context that persists and context that changes with each interaction.

Why: Optimizes window usage and maintains consistency.

Concepts: Static context, dynamic injection, context refresh.

What it is: Maintain focus when lots of context is available.

Why: More context isn't always better—focus is essential.

Concepts: Scope boundaries, attention steering, relevance filtering.

What it is: Anti-patterns and problems caused by excessive or poorly organized context.

Why: "Lost in the middle" effect, instruction dilution, conflicts.

Concepts: Context overflow, attention dilution, instruction drift.

What it is: Internal attention mechanisms in long contexts.

Why: Understanding limitations helps you optimize usage.

Concepts: Attention patterns, recency bias, position encoding.

What it is: When to use the full window vs. selective retrieval.

Why: With 1M+ tokens, RAG isn't always necessary.

Concepts: Full context loading, selective retrieval, hybrid approaches.

What it is: How to efficiently structure 100k-1M tokens.

Why: Organization directly impacts response quality.

Concepts: Document ordering, section markers, table of contents.

What it is: Techniques for keeping essential information in fewer tokens.

Why: Preserves knowledge when the context window is insufficient.

Concepts: Hierarchical summarization, semantic compression, lossy vs lossless.

What it is: Decide what to keep and what to discard when space is limited.

Why: Intelligent prioritization is critical to effectiveness.

Concepts: Relevance scoring, recency weighting, importance markers.

What it is: Common errors when working with massive contexts.

Why: Avoiding pitfalls that degrade performance.

Concepts: Context dumping, lost in the middle, noise injection.

What it is: Modular, reusable capabilities that define behaviors.

Why: Skills turn prompts from commands into capabilities.

Concepts: Skill definition, capability boundaries, skill interface.

What it is: Structure prompts that define skills, not just tasks.

Why: Skills are generalizable; tasks are one-off.

Concepts: Skill prompt template, capability scope, generalization.

What it is: Create skills that work across multiple contexts and combinations.

Why: Reuse speeds up development and ensures consistency.

Concepts: Modular skills, skill composition, inheritance patterns.

What it is: Skills optimized for specific domains (coding, analysis, writing).

Why: Specialization improves quality in specific areas.

Concepts: Domain expertise, specialized prompts, vertical skills.

What it is: Control which skills are active at any given time.

Why: Conflicting skills can degrade results.

Concepts: Skill toggling, conditional activation, context-aware loading.

What it is: Manage versions and the evolution of skills over time.

Why: Enables rollback, A/B testing, and continuous improvement.

Concepts: Semantic versioning, changelog, migration strategies.

What it is: Integrate different context sources into a coherent system.

Why: Real-world applications require multiple sources of information.

Concepts: Context merging, source attribution, priority rules.

What it is: Select and combine the appropriate skills for each task.

Why: Complex tasks require multiple skills.

Concepts: Skill routing, task decomposition, skill chaining.

What it is: Define behaviors through context instead of traditional programming.

Why: Flexibility and faster iteration.

Concepts: Context-driven flows, declarative behavior, prompt-as-code.

What it is: Identify and resolve conflicts between different instructions/skills.

Why: Conflicts lead to unpredictable behavior.

Concepts: Conflict detection, resolution strategies, priority chains.

What it is: Build processing chains using context as the connection.

Why: Enables complex pipelines without additional code.

Concepts: Context chaining, output-to-input, state propagation.

What it is: Examples of implementation in real products.

Why: Connects theory with professional practice.

Concepts: Case studies, production patterns, lessons learned.

What it is: Criteria for deciding whether to use RAG or full context.

Why: RAG isn’t obsolete, but its role has changed.

Concepts: Scale thresholds, freshness needs, precision requirements.

What it is: Use RAG to enrich extensive context, not replace it.

Why: Combining them offers the best of both worlds.

Concepts: Hybrid retrieval, context augmentation, selective injection.

What it is: Add specific knowledge only when necessary.

Why: Prevents context pollution with irrelevant information.

Concepts: Just-in-time retrieval, query-aware injection, relevance gates.

What it is: Architecture that combines static and dynamic context.

Why: Balances stability with updating.

Concepts: Static base, dynamic overlay, context composition.

What it is: Use context strategically to reduce fabrication.

Why: Adequate context is the best defense against hallucinations.

Concepts: Grounding, source attribution, confidence calibration.

What it is: Metrics and methods for evaluating context effectiveness.

Why: Without metrics, there's no systematic improvement.

Concepts: Context relevance, utilization rate, signal-to-noise ratio.

What it is: Treat prompts as software components, not scripts.

Why: Enables design patterns and applied software engineering.

Concepts: Prompt components, interfaces, contracts, dependencies.

What it is: Define behavioral expectations through formal context.

Why: Contracts enable verification and guarantees.

Concepts: Behavioral contracts, invariants, preconditions, postconditions.

What it is: Apply software engineering practices to skills.

Why: Professionalizes prompt development.

Concepts: Testing, documentation, CI/CD for prompts, observability.

What it is: What you know now and what you still need to learn.

Why: Honest self-assessment is essential for growth.

Concepts: Competency map, skill gaps, learning path.

What it is: Preview of the next level and its expectations.

Why: Mentally prepares you for the next step.

Concepts: Advanced architecture, multi-agent systems, enterprise production.

What it is: Final project that brings together all the concepts in the learning path.

Why: Demonstrates full competence at the advanced level.

Concepts: System design, implementation, evaluation, documentation.