Orchestration Family (G3)
These elements coordinate multiple components into working systems.
You can't orchestrate one thing—this family only exists through combination. Context windows hold the information, RAG fills them intelligently, frameworks provide the plumbing, and protocols standardize connections.
| Element | Name | Row | Description |
|---|---|---|---|
| Cw | Context Windows | Primitives | The limited space an LLM can "see" |
| Rg | RAG | Compositions | Retrieval-Augmented Generation |
| Fw | Frameworks | Deployment | Platforms connecting AI components |
| Mc | MCP | Emerging | Model Context Protocol and standards |
Cw — Context Windows
Position in Periodic Table:
G3: Orchestration Family
┌──────────────────────────┐
│ → [Context Windows] │ Row 1: Primitives
│ RAG │ Row 2: Compositions
│ Frameworks │ Row 3: Deployment
│ MCP │ Row 4: Emerging
└──────────────────────────┘
What It Is
Context windows are the limited space an LLM can "see" at once. Managing what goes into context is fundamental orchestration. Every token costs money and attention. Think of it as the model's working memory.
Why It Matters
Understanding context windows is crucial because:
- There's a hard limit on what the model can process
- Not all context is equal—position and relevance matter
- Token costs scale linearly with context size
- "Lost in the middle" phenomenon affects retrieval
- Context management is often the difference between success and failure
Context Window Sizes (2026)
| Model | Context Window |
|---|---|
| GPT-4 Turbo | 128K tokens |
| Claude 3 | 200K tokens |
| Gemini 1.5 | 1M+ tokens |
| Llama 3 | 8K-128K tokens |
What Consumes Context
| Component | Description |
|---|---|
| System prompt | Instructions, persona, rules |
| Conversation history | Previous messages |
| Retrieved context | RAG results, documents |
| User input | Current message |
| Output | Generated response |
The "Lost in the Middle" Problem
Research shows models pay most attention to:
- The beginning of context (primacy)
- The end of context (recency)
- Less attention to the middle
Implication: Put important information at the start or end, not buried in the middle.
Context Management Strategies
- Compression: Summarize long content to preserve meaning in fewer tokens
- Selection: Only include what's relevant to the current query
- Chunking: Break documents into pieces, retrieve only relevant chunks
- Sliding Window: Keep recent history, summarize or drop older content
- Hierarchical: Summary of full context + detailed recent context
Tier Relevance
| Tier | Expectation |
|---|---|
| Foundation | Understand context limits and token costs |
| Practitioner | Implement context management strategies |
| Expert | Optimize context for cost/quality tradeoffs |
Rg — RAG
Position in Periodic Table:
G3: Orchestration Family
┌──────────────────────────┐
│ Context Windows │ Row 1: Primitives
│ → [RAG] │ Row 2: Compositions
│ Frameworks │ Row 3: Deployment
│ MCP │ Row 4: Emerging
└──────────────────────────┘
What It Is
RAG (Retrieval-Augmented Generation) combines retrieval with generation. A question comes in, relevant context is retrieved (via embeddings/vector DB), the prompt is augmented with that context, and the LLM generates a grounded answer.
Why It Matters
RAG solves critical LLM limitations:
- Hallucination: Grounds answers in retrieved facts
- Stale knowledge: Access up-to-date information
- Domain specificity: Query your proprietary data
- Traceability: Know where answers came from
The RAG Pipeline
User Query → Embed → Search Vector DB → Retrieve Docs
↓
Retrieved Docs + Query → Augmented Prompt → LLM → Grounded Response
Key Components
| Component | Purpose |
|---|---|
| Embedding model | Convert query to vector |
| Vector database | Store and search documents |
| Retriever | Find relevant documents |
| Reranker | Improve retrieval ordering |
| Generator | LLM that produces the answer |
Chunking Strategies
| Strategy | Description | Best For |
|---|---|---|
| Fixed size | Split every N characters | Simple, predictable |
| Sentence | Split on sentence boundaries | Readability |
| Paragraph | Split on paragraph breaks | Structured documents |
| Semantic | Split on topic changes | Mixed content |
| Recursive | Try multiple strategies | General purpose |
Advanced Techniques
| Technique | Description |
|---|---|
| Hybrid search | Combine vector + keyword search |
| Query expansion | Rephrase query for better retrieval |
| Reranking | Use cross-encoder to reorder results |
| Multi-query | Generate multiple queries, merge results |
| Self-RAG | Model decides when to retrieve |
Common Pitfalls
- Poor chunking: Chunks that split important context
- No reranking: First result isn't always best
- Ignoring metadata: Missing filtering opportunities
- Too few/many chunks: Balance context vs. noise
- No evaluation: Not measuring retrieval quality
Tier Relevance
| Tier | Expectation |
|---|---|
| Foundation | Understand the RAG pattern conceptually |
| Practitioner | Build complete RAG pipelines |
| Expert | Optimize retrieval quality and implement advanced patterns |
Fw — Frameworks
Position in Periodic Table:
G3: Orchestration Family
┌──────────────────────────┐
│ Context Windows │ Row 1: Primitives
│ RAG │ Row 2: Compositions
│ → [Frameworks] │ Row 3: Deployment
│ MCP │ Row 4: Emerging
└──────────────────────────┘
What It Is
AI frameworks are platforms like LangChain, LlamaIndex, and others that provide the plumbing to connect AI components. They handle the complexity of building and deploying AI systems.
Why It Matters
Frameworks accelerate development by:
- Providing battle-tested implementations
- Abstracting away boilerplate
- Enabling rapid prototyping
- Standardizing patterns across teams
- Integrating with various providers
Major Frameworks (2026)
| Framework | Focus | Best For |
|---|---|---|
| LangChain | General-purpose chains | Flexible AI applications |
| LlamaIndex | Data indexing/retrieval | RAG-heavy applications |
| Haystack | NLP pipelines | Search and QA |
| Semantic Kernel | Microsoft ecosystem | .NET/enterprise |
| LangGraph | Stateful agents | Complex agent workflows |
| CrewAI | Multi-agent | Agent teams |
Common Abstractions
| Abstraction | Purpose |
|---|---|
| Chains | Sequence of operations |
| Agents | Autonomous decision-making |
| Tools | Functions agents can call |
| Memory | Conversation state |
| Retrievers | Document retrieval |
When to Use Frameworks
Use frameworks when:
- Rapid prototyping is priority
- Using standard patterns (RAG, agents)
- Team benefits from shared abstractions
- Integrating multiple providers
Consider direct APIs when:
- Simple, single-model use case
- Maximum control needed
- Framework overhead is concern
- Highly custom patterns
Framework Tradeoffs
| Aspect | Pros | Cons |
|---|---|---|
| Abstraction | Faster development | Less control |
| Updates | Community improvements | Breaking changes |
| Debugging | Logging built-in | Stack traces can be opaque |
Tier Relevance
| Tier | Expectation |
|---|---|
| Foundation | Awareness of major frameworks |
| Practitioner | Proficiency in at least one framework |
| Expert | Evaluate frameworks, know when to use vs. build |
Mc — MCP
Position in Periodic Table:
G3: Orchestration Family
┌──────────────────────────┐
│ Context Windows │ Row 1: Primitives
│ RAG │ Row 2: Compositions
│ Frameworks │ Row 3: Deployment
│ → [MCP] │ Row 4: Emerging
└──────────────────────────┘
What It Is
MCP (Model Context Protocol) and similar standards are emerging protocols for how AI systems connect to tools and data sources. Think of it as the USB standard of AI integration.
Why It Matters
Without standards, every AI-tool integration is custom. MCP and similar protocols enable:
- Reusable tool implementations
- Interoperability across AI systems
- Easier ecosystem development
- Reduced integration complexity
The Problem MCP Solves
Before: Each AI system needs custom code for each tool
Claude ──custom──> Slack
Claude ──custom──> GitHub
GPT ────custom──> Slack (different code!)
GPT ────custom──> GitHub (different code!)
After: Tools expose standard interface, any AI can use
Claude ──MCP──┐
├──> Slack Server (one implementation)
GPT ────MCP──┘
MCP Architecture
| Component | Role |
|---|---|
| Host | AI application (Claude, IDEs, etc.) |
| Server | Tool provider (Slack, GitHub, DB) |
| Protocol | Standardized communication format |
| Resources | Data the server exposes |
| Tools | Actions the server enables |
Current State (2026)
MCP was introduced by Anthropic in late 2024 and is gaining adoption. The ecosystem is growing, but:
- Not all providers support it yet
- Some features are still evolving
- Alternative standards may emerge
- Best practices are still forming
Evaluating Protocol Adoption
| Factor | Question |
|---|---|
| Ecosystem | Are servers available for your tools? |
| Support | Does your AI platform support it? |
| Maturity | Is the protocol stable enough? |
| Security | Does it meet your security requirements? |
Tier Relevance
| Tier | Expectation |
|---|---|
| Foundation | Awareness of emerging standards |
| Practitioner | Use existing MCP servers |
| Expert | Evaluate protocols, build custom servers |