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== Model Context Protocol (MCP) == | == Model Context Protocol (MCP) == | ||
A '''Model Context Protocol (MCP)''' is a protocol that '''standardizes communication between Large Language Models''' (LLMs) and ''' external systems''' , such as ITSM tools (like ServiceNow), Kubernetes clusters, and more. | A '''Model Context Protocol (MCP)''' is a protocol that '''standardizes communication between Large Language Models''' (LLMs) trough an '''Agent AI''' and ''' external systems''' , such as ITSM tools (like ServiceNow), Kubernetes clusters, and more. | ||
You can use an ''' MCP client''' , for example, '''Continue.dev''' in your IDE (like VS Code) and then '''configure MCP servers''', such as your Kubernetes cluster, to enable your LLM to interact with these systems. | You can use an ''' MCP client''' , for example, '''Continue.dev''' in your IDE (like VS Code) and then '''configure MCP servers''', such as your Kubernetes cluster, to enable your LLM to interact with these systems. | ||
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== Prompt Engineering == | == Prompt Engineering == | ||
Prompt engineering is the practice of '''crafting clear''', '''structured instructions''' to '''guide AI models''' toward producing optimal outputs. | Prompt engineering is the practice of '''crafting clear''', '''structured instructions''' to '''guide AI models''' toward producing optimal outputs. Based on my experience : | ||
* '''Rule 1 :''' '''DON'T''' write '''too much text at once'''; if possible, break the work into '''sequences'''. Otherwise, '''use functions''' of the app. | * '''Rule 1 :''' '''DON'T''' write '''too much text at once'''; if possible, break the work into '''sequences'''. Otherwise, '''use functions''' of the app. | ||
* '''Rule 2 :''' '''DON'T''' ask the '''AI to write prompt engineering instructions for another AI''', it creates an infinite loop and wastes time. | * '''Rule 2 :''' '''DON'T''' ask the '''AI to write prompt engineering instructions for another AI''', it creates an infinite loop and wastes time. | ||
* '''Rule 3 :''' 🎯 '''Use emojis''' to clarify and sequence your prompt, helping the '''AI recognise when there's a new instruction'''. | |||
🎯 '''Use emojis''' to clarify and sequence your prompt, helping the '''AI recognise when there's a new instruction'''. | |||
Dernière version du 23 décembre 2025 à 21:57
Notions
There are other notions that are important to understand in relation with AI technologies.
Large Language Model (LLM)
A Large Language Model (LLM) is the engine behind an AI application such as ChatGPT. In this case, the engine powering ChatGPT is GPT-4 (or GPT-4o, previously), which is the LLM used by the application.
Azure AI Foundry is a service that allows you to choose which Large Language Model (LLM) you want to use.
Model Context Protocol (MCP)
A Model Context Protocol (MCP) is a protocol that standardizes communication between Large Language Models (LLMs) trough an Agent AI and external systems , such as ITSM tools (like ServiceNow), Kubernetes clusters, and more.
You can use an MCP client , for example, Continue.dev in your IDE (like VS Code) and then configure MCP servers, such as your Kubernetes cluster, to enable your LLM to interact with these systems.
Retrieval-Augmented Generation (RAG)
Low-Code / No-Code
Prompt Engineering
Prompt engineering is the practice of crafting clear, structured instructions to guide AI models toward producing optimal outputs. Based on my experience :
- Rule 1 : DON'T write too much text at once; if possible, break the work into sequences. Otherwise, use functions of the app.
- Rule 2 : DON'T ask the AI to write prompt engineering instructions for another AI, it creates an infinite loop and wastes time.
- Rule 3 : 🎯 Use emojis to clarify and sequence your prompt, helping the AI recognise when there's a new instruction.