πOutput:
πThis same pattern works for Groq, OpenAI, Anthropic and Open source models like HugginFace.
β¨just swap the importn and initialization of the model
#LangChain#GoogleGemini#Python#GenAI#LearningInPublic
The LLM has zero persistent memory.
Your app manually builds and passes this array on every call.
That growing array = the "memory" of your chatbot.
For Agentic AI, managing this array across 10+ steps is a core engineering challenge.
#LLM#GenerativeAI#AgenticAI#python
π΄ ASSISTANT role β the reply
β’ The AI's generated response
β’ Shaped by BOTH system + user
β’ Previous assistant messages are fed back into the next request
This is the secret behind "memory":
The model reads ALL of this every single time.
#LLM#GenerativeAI#AgenticAI
That's it. 3 concepts every AI builder must know:
β LLM β the brain
β Tokens β what it reads
β Context Window β how much it remembers
Follow for more beginner-friendly AI breakdowns π
#AI#LLM#AIBasics#GenerativeAI#MachineLearning
AI doesn't read your words the way you do.
It uses something called an LLM β and 3 concepts will change how you think about it forever.
π§΅ Thread π
#AI#LLM#MachineLearning#Thread
π‘ Why should you care?
If you're building: β Chatbots β RAG pipelines β AI Agents
The context window is the #1 resource you'll always be managing.
Understand this early. It saves a lot of debugging later.
#AIAgents#RAG#LLMDev#GenerativeAI
Most people use AI every day but don't know AI fundamentals! π
π§ What is an LLM?
π’ Tokens vs. Words
π Why Context Windows matter
Perfect for beginners or devs starting their AI journey.
My Medium article: https://t.co/6j3LREwwzC
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