Certified Context Engineer

•What context engineering in AI means in modern AI systems

•Prompt engineering vs context engineering vs AI application architecture

•The LLM application stack: model, prompt, context, tools, data, and workflow

•The six context inputs: instructions, user intent, history, retrieved knowledge, tools, and metadata

•The LLM-as-operating-system analogy: model as reasoning engine, context window as working memory

•Typical business use cases: assistants, copilots, RAG systems, agents, and enterprise automation

•Tokens, context windows, and model input limits

•Instruction hierarchy: system, developer, user, retrieved, and tool messages

•Attention, recency bias, and position sensitivity

•Context overload, distraction, and context rot

•Latency, cost, and quality trade-offs in long-context applications

•Designing context for clarity, sufficiency, relevance, and economy

•Role, task, context, format, and constraint patterns

•Few-shot, example-driven, and counter-example prompting

•Chain-of-thought alternatives: reasoning scaffolds, rubrics, and structured deliberation

•Step-back, self-check, and critique-revision patterns

•Reusable prompt templates and prompt versioning

•Designing context blocks for different personas, domains, and tasks

•Context layers: policy, task, domain knowledge, user state, tools, and output rules

•Instruction priority and conflict resolution

•Context mapping: identifying what the model must know, may know, and must not access

•Context isolation and quarantine for untrusted content

•Metadata-driven context assembly

•Context portability across models, teams, and workflows

•Using structured context blocks with headings, XML-like tags, and markdown sections

•JSON schema, function schemas, and typed outputs

•Format enforcement, validation, and retry loops

•Separating instructions, evidence, examples, and user data

•Designing context for APIs, workflows, and downstream systems

•Handling ambiguity, missing information, and escalation behavior

•RAG as context construction rather than simple document search

•Document ingestion, cleaning, and normalization

•Chunking strategies: fixed-size, semantic, hierarchical, and sliding-window chunks

•Embeddings, vector search, and semantic similarity

•Vector databases and indexes: Pinecone, Weaviate, pgvector, FAISS, and similar tools

•Grounded response generation with citations and source traceability

•Hybrid search: keyword, semantic, and metadata filtering

•Query transformation, expansion, and decomposition

•Re-ranking, relevance scoring, and evidence selection

•Context assembly pipelines and retrieval orchestration

•Handling noisy, duplicated, and contradictory sources

•Knowledge freshness, provenance, and confidence scoring

•Conversation memory and session state

•Short-term vs long-term memory design

•Episodic, semantic, and procedural memory

•User preference stores and enterprise knowledge stores

•Memory write, update, retrieval, and deletion rules

•Privacy, consent, and safe handling of remembered information

•Token budgeting and context cost estimation

•Sliding windows, rolling summaries, and state compaction

•Selective inclusion, truncation, and prioritization strategies

•Compression methods including summarization, extractive compression, and learned compression

•Preventing loss of critical facts during summarization

•Measuring quality impact after compression

•Function calling and tool schemas

•Tool descriptions, argument design, and result handling

•Model Context Protocol concepts and use cases

•Building and consuming MCP servers for data, files, APIs, and developer tools

•Tool selection, tool routing, and tool execution constraints

•Securing tool access with authentication, authorization, and audit trails

•Agent loops: plan, act, observe, reflect, and revise

•Context routing for task-specific agents and sub-agents

•Multi-agent collaboration, delegation, and handoff patterns

•Workflow orchestration using frameworks such as LangGraph, AutoGen, and similar tools

•State management, checkpoints, and recovery in agent workflows

•Human-in-the-loop review for high-risk actions

•Prompt injection, jailbreaks, and indirect injection attacks

•Data leakage risks in RAG, memory, and tool-enabled systems

•Context isolation, input sanitization, and policy enforcement

•Guardrails for content, actions, tools, and data access

•Enterprise governance: ownership, approvals, change control, and auditability

•Responsible AI considerations for transparency, fairness, and compliance

•Evaluation dimensions: relevance, sufficiency, faithfulness, provenance, safety, and usability

•Hallucination detection and groundedness testing

•Retrieval evaluation: precision, recall, MRR, and hit rate

•LLM-as-judge, rubric-based testing, and human review

•A/B testing for prompt and context versions

•Observability tools such as LangSmith, tracing, logs, and feedback loops

•Define a business problem and context engineering scope

•Design context architecture, RAG pipeline, memory policy, and tool interfaces

•Create security, governance, and human-review controls

•Build an evaluation plan with measurable quality criteria

•Prepare a deployment-ready solution blueprint and demo

•Present trade-offs around cost, latency, risk, and user experience