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|**Weights** <br/> 2022-2023 | Parametric knowledge baked into the model. <br/> `Themes: Pretraining, Scaling Laws, Fine-tuning, RLHF, Alignment, Instruction-following, Few-shot`| Foundations: [Large Language Model Landscape](section/models_research.md#large-language-model-landscape), [Large Language Model Collection](section/models_research.md#large-language-model-collection), [Foundation Model Providers](section/models_research.md#foundation-model-providers) <br/> Training: [Large Language Model Training and Optimization](section/models_research.md#large-language-model-training-and-optimization), [Model Training & Inference](section/azure.md#model-training--inference), [Training & Fine-tuning](section/applications.md#training--fine-tuning) <br/> Behavior and safety: [Trust, Safety, and Security](section/models_research.md#trust-safety-and-security), [Safety, Security & LLMOps](section/azure.md#safety-security--llmops)|
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|**Context** <br/> 2023-2024 | What the model sees at inference time. <br/> `Themes: Prompting, Chain-of-Thought, RAG, Memory, Long Context, Knowledge Injection, Context Engineering`| Prompting: [Prompt Engineering and Visual Prompts](section/models_research.md#prompt-engineering-and-visual-prompts), [Prompt Engineering & Tooling](section/azure.md#prompt-engineering--tooling) <br/> Retrieval: [RAG](section/applications.md#rag-retrieval-augmented-generation), [Azure AI Search](section/azure.md#azure-ai-search), [RAG Best Practices](section/best_practices.md#rag-best-practices) <br/> Memory and context windows: [Context and Long-Context Limits](section/models_research.md#context-and-long-context-limits), [Memory](section/applications.md#memory), [Data Processing & Memory](section/azure.md#data-processing--memory)|
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| **Harness** <br/> 2025-2026 | How the agent acts in the real world. <br/> `Themes: Function Calling, Tool Ecosystems, MCP, Skills, Workflow Graphs, Multi-agent, A2A protocols, Orchestration, Agent Infrastructure, Security` | Agent runtime: [AI Application](section/applications.md#ai-application), [Agent Frameworks](section/azure.md#agent-frameworks), [Agent Development](section/azure.md#agent-development), [Agent Best Practices](section/best_practices.md#agent-best-practices) <br/> Protocols and tools: [Agent Protocol](section/applications.md#agent-protocol), [Coding & Research](section/applications.md#coding--research), [Skill](section/applications.md#skill), [Harness](section/applications.md#harness), [Dev Tools, MCP & Extensions](section/azure.md#dev-tools-mcp--extensions) <br/> Apps and operations: [Evaluating Large Language Models](section/tools_extra.md#evaluating-large-language-models), [LLMOps](section/tools_extra.md#llmops-large-language-model-operations), [Learning Resources & Workshops](section/azure.md#learning-resources--workshops), [Code Samples & Workshops](section/azure.md#code-samples--workshops) |
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| **Harness** <br/> 2025-2026 | How the agent acts in the real world. <br/> `Themes: Function Calling, Tool Ecosystems, MCP, Skills, Workflow Graphs, Multi-agent, A2A protocols, Orchestration, Agent Infrastructure, Security` | Agent runtime: [AI Application](section/applications.md#ai-application), [Agent Frameworks](section/azure.md#agent-frameworks), [Agent Development](section/azure.md#agent-development), [Agent Best Practices](section/best_practices.md#agent-best-practices) <br/> Protocols and tools: [Agent Protocol](section/applications.md#agent-protocol), [Coding & Research](section/applications.md#coding--research), [Skills](section/applications.md#skills), [Harness](section/applications.md#harness), [Loop Engineering](section/applications.md#loop-engineering), [Dev Tools, MCP & Extensions](section/azure.md#dev-tools-mcp--extensions) <br/> Apps and operations: [Evaluating Large Language Models](section/tools_extra.md#evaluating-large-language-models), [LLMOps](section/tools_extra.md#llmops-large-language-model-operations), [Learning Resources & Workshops](section/azure.md#learning-resources--workshops), [Code Samples & Workshops](section/azure.md#code-samples--workshops) |
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Refereces: [DailyDoseOfDS - *Evolution of the Agent Landscape*](https://blog.dailydoseofds.com/p/evolution-of-agent-landscape-from)
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@@ -55,7 +55,9 @@ Refereces: [DailyDoseOfDS - *Evolution of the Agent Landscape*](https://blog.dai
| Data & Analytics | Build a data or analytics agent |[Data & Analytics Agents](section/applications.md#data--analytics-agents) → [Data Processing & OCR](section/applications.md#data-processing--ocr) → [Memory](section/applications.md#memory) → [Tool Use](section/best_practices.md#tool-use) → [Evaluating Large Language Models](section/tools_extra.md#evaluating-large-language-models)|
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| Local LLMs | Build a local or self-hosted LLM application |[Large Language Model Collection](section/models_research.md#large-language-model-collection) → [Model Serving & Local Runtimes](section/applications.md#model-serving--local-runtimes) → [Model Gateway](section/applications.md#model-gateway) → [UI & No-Code Tool](section/applications.md#ui--no-code-tool) → [Observability & LLMOps](section/applications.md#observability--llmops)|
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