Listen to this Post
Large Language Models (LLMs) have transformed AI, but their real power emerges through augmentation, structuring, and orchestration. Here’s how AI systems are evolving:
1. Basic LLMs (Prompt → Response)
The foundation: Input a prompt, and the model generates a response. Limited by static knowledge and no memory.
2. RAG (Retrieval-Augmented Generation)
Enhances LLMs by fetching external data (e.g., vector databases) to ground responses in real-time context. Critical for AI search and chatbots.
3. Agentic LLMs (Autonomous Reasoning + Tools)
The next frontier: AI agents think, plan, and act using:
– Tools (APIs, code execution, search)
– Memory (short/long-term context)
– Reasoning chains (e.g., ReAct, Tree of Thought)
– Dynamic decision-making
You Should Know:
RAG Implementation (Code Example)
from langchain.document_loaders import WebBaseLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
Load & index documents
loader = WebBaseLoader("https://example.com")
docs = loader.load()
db = FAISS.from_documents(docs, OpenAIEmbeddings())
Retrieve context
retriever = db.as_retriever()
context = retriever.invoke("Query here")
Agentic Workflow with LangChain
from langchain.agents import AgentExecutor, create_react_agent
from langchain.tools import Tool
def search_api(query):
return "API results"
tools = [Tool(name="Search", func=search_api, description="Searches APIs")]
agent = create_react_agent(llm, tools, prompt_template)
agent_executor = AgentExecutor(agent=agent, tools=tools)
agent_executor.invoke({"input": "Task"})
Key Linux/IT Commands for AI Systems
- Vector DB Management:
redis-cli --vectorize Redis for vector storage
- API Orchestration:
curl -X POST http://localhost:5000/agent -H "Content-Type: application/json" -d '{"query":"..."}' - Model Serving:
docker run -p 8000:8000 ollama/llama2 Local LLM deployment
What Undercode Says
Agentic AI demands robust infrastructure:
- Memory: Use PostgreSQL with `pgvector` for scalable context storage.
- Tooling: Wrap APIs with FastAPI for agent access.
- Monitoring: Prometheus + Grafana for agent performance metrics.
4. Security: Isolate agents in Docker/Kubernetes sandboxes.
Expected Output: Autonomous AI workflows with self-optimizing toolchains, powered by:
– LangGraph for multi-agent coordination.
– Ollama for local LLM ops.
– AutoGen for recursive task-solving.
Expected Output: A deployable AI agent system integrating RAG, tool use, and real-time reasoning.
References:
Reported By: Brijpandeyji Large – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
