Ai-Ml

The Evolution of AI: From Chat to Agents We’re moving beyond simple Q&A chatbots to agentic AI—systems that can: Understand complex, multi-step tasks Plan a sequence of actions to achieve goals Execute those actions using tools and APIs Adapt when things don’t go as expected Learn from feedback and improve over time This is where the real value of AI lies—automation of complex knowledge work. My Agent Development Framework 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 from langgraph.graph import StateGraph, END from pydantic import BaseModel from typing import Literal class AgentState(BaseModel): task: str plan: list[str] = [] current_step: int = 0 results: dict = {} requires_human: bool = False def create_reliable_agent(): workflow = StateGraph(AgentState) # Nodes workflow.add_node("planner", plan_task) workflow.add_node("validator", validate_plan) workflow.add_node("executor", execute_step) workflow.add_node("checker", check_result) workflow.add_node("human_review", request_human_review) workflow.add_node("compiler", compile_results) # Edges with conditional routing workflow.add_edge("planner", "validator") workflow.add_conditional_edges( "validator", lambda s: "executor" if s.plan else "planner" ) workflow.add_conditional_edges( "checker", lambda s: "human_review" if s.requires_human else "executor" ) workflow.add_edge("executor", "checker") workflow.add_edge("human_review", "executor") return workflow.compile() Agent Patterns I Implement Pattern Use Case Example ReAct Reasoning + Acting Research agent that searches, reads, summarizes Plan-Execute Complex multi-step tasks Document processing pipeline Supervisor Multi-agent coordination Team of specialized analysts Human-in-the-Loop High-stakes decisions Financial approvals, legal review Reflexion Self-improvement Agent that learns from mistakes Tools and Integrations I build agents that can use: ...

Claude Integration Example 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 from anthropic import Anthropic client = Anthropic() def analyze_document(document: str) -> dict: """Analyze long document using Claude's 200K context.""" response = client.messages.create( model="claude-3-5-sonnet-20241022", max_tokens=4096, messages=[{ "role": "user", "content": f""" Analyze this document and provide structured output. <document> {document} </document> Respond in XML format: <analysis> <summary>Brief summary</summary> <key_points> <point>Key point 1</point> <point>Key point 2</point> </key_points> <entities> <entity type="person">Name</entity> <entity type="org">Organization</entity> </entities> <sentiment>positive/negative/neutral</sentiment> </analysis> """ }] ) return parse_xml_response(response.content[0].text) Claude vs OpenAI Comparison Feature Claude 3.5 Sonnet GPT-4 Turbo Context 200K tokens 128K tokens Structured Output XML preferred JSON mode Safety Constitutional AI RLHF Speed Very fast Fast Best For Documents, analysis Code, general Related Experience: ...

Why LangChain Matters for Your Business LangChain has become the de facto framework for building LLM applications because it solves the hard problems: Composability: Chain together multiple AI operations reliably Integrations: Connect to 100+ data sources, tools, and LLM providers Observability: Debug and monitor complex AI workflows Community: Massive ecosystem of templates, tools, and best practices But here’s the catch: LangChain is easy to start with and hard to master. Most tutorials show simple demos that break in production. That’s where my expertise comes in. ...

MCP Server Example 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 from mcp import Server, Tool, Resource server = Server("enterprise-tools") @server.tool() async def search_documents( query: str, filters: dict = None, limit: int = 10 ) -> list[dict]: """Search company documents with semantic understanding.""" results = await vector_store.similarity_search( query=query, k=limit, filter=filters ) return [ {"title": r.title, "content": r.content, "source": r.source} for r in results ] @server.resource("database://customers/{customer_id}") async def get_customer(customer_id: str) -> dict: """Retrieve customer information by ID.""" customer = await db.customers.find_one({"id": customer_id}) return customer # Run with proper authentication server.run(auth=enterprise_auth_middleware) MCP Use Cases Use Case Tools/Resources Example Knowledge Base Document search, Q&A Answer questions from docs Database Access SQL queries, CRUD AI-powered data analysis API Integration External service calls Booking, CRM updates File System Read/write files Code generation, reports Communication Email, Slack, etc. Automated notifications Related Experience: ...

LLM Integration Architecture 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 from openai import AsyncOpenAI from anthropic import AsyncAnthropic from pydantic import BaseModel from tenacity import retry, stop_after_attempt, wait_exponential class LLMRouter: """Route requests to optimal LLM based on task complexity""" def __init__(self): self.openai = AsyncOpenAI() self.anthropic = AsyncAnthropic() @retry(stop=stop_after_attempt(3), wait=wait_exponential()) async def generate(self, prompt: str, complexity: str = "auto") -> str: model = self._select_model(prompt, complexity) try: if model.startswith("gpt"): return await self._call_openai(prompt, model) elif model.startswith("claude"): return await self._call_anthropic(prompt, model) except Exception as e: # Fallback to alternative provider return await self._fallback_generate(prompt, model, e) def _select_model(self, prompt: str, complexity: str) -> str: if complexity == "high" or len(prompt) > 10000: return "claude-3-opus-20240229" elif complexity == "medium": return "gpt-4-turbo" else: return "gpt-3.5-turbo" async def _call_openai(self, prompt: str, model: str) -> str: response = await self.openai.chat.completions.create( model=model, messages=[{"role": "user", "content": prompt}], temperature=0.7 ) return response.choices[0].message.content LLM Provider Comparison Provider Best For Strengths OpenAI GPT-4 Complex reasoning, code Accuracy, function calling Claude 3 Long documents, analysis 200K context, safety GPT-3.5 Turbo Simple tasks, high volume Speed, cost Gemini Multimodal, Google integration Long context, grounding Llama/Mistral Self-hosted, privacy No data sharing, cost Cost Optimization Strategies I’ve helped companies reduce LLM costs by 40-50%: ...

Prompt Engineering Patterns 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 from pydantic import BaseModel from openai import OpenAI # Structured Output with Validation class DocumentAnalysis(BaseModel): summary: str key_entities: list[str] sentiment: str confidence: float citations: list[dict] ANALYSIS_PROMPT = """ Analyze the following document and extract structured information. <document> {document_text} </document> Think through this step by step: 1. First, identify the main topic and purpose 2. Extract key entities (people, organizations, dates) 3. Determine overall sentiment 4. Note any citations or references Respond with valid JSON matching this schema: {schema} Be precise and include confidence scores. """ def analyze_document(text: str) -> DocumentAnalysis: response = client.chat.completions.create( model="gpt-4-turbo", messages=[{ "role": "user", "content": ANALYSIS_PROMPT.format( document_text=text, schema=DocumentAnalysis.model_json_schema() ) }], response_format={"type": "json_object"} ) return DocumentAnalysis.model_validate_json(response.choices[0].message.content) Prompt Optimization Checklist Technique Impact When to Use Be specific Quality ↑ Always Show examples Consistency ↑ Complex formats Chain of thought Accuracy ↑ Reasoning tasks Output schema Reliability ↑ Data extraction Temperature tuning Control ↑ Balance creativity/consistency Token reduction Cost ↓ High-volume applications Related Experience: ...

What is RAG and Why Does It Matter? Retrieval-Augmented Generation (RAG) is the technique that makes LLMs useful for your specific data. Instead of relying solely on the model’s training data, RAG: Retrieves relevant documents from your knowledge base Augments the LLM prompt with this context Generates accurate, grounded responses This solves the fundamental problem of LLMs: they don’t know your business. RAG bridges that gap. The RAG Architecture Spectrum 1 2 3 4 5 6 Simple RAG → Advanced RAG → Production RAG ───────────────────────────────────────────────────────────────────────────── Chunk + Embed Hybrid Search Multi-stage Pipeline Single Vector Store Re-ranking Caching + Streaming Basic Prompt Query Transformation Observability No Citations Source Tracking A/B Testing I specialize in building Production RAG systems that actually work in enterprise environments. ...

Vector Database Architecture 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 from langchain_community.vectorstores import PGVector from langchain_openai import OpenAIEmbeddings # Production PGVector Setup embeddings = OpenAIEmbeddings(model="text-embedding-3-large") vectorstore = PGVector.from_connection_string( connection_string=DATABASE_URL, embedding=embeddings, collection_name="documents", pre_delete_collection=False ) # Hybrid search with metadata filtering results = vectorstore.similarity_search_with_score( query="patent infringement claims", k=10, filter={"category": "patents", "year": {"$gte": 2020}} ) When to Use Each Vector Database Use Case Recommended Reason PostgreSQL shop PGVector Unified infrastructure Scale + low ops Pinecone Fully managed Complex filters Qdrant Advanced filtering Prototyping Chroma Simple setup Self-hosted Qdrant/Milvus Full control Related Experience: ...

Building Before the Playbook Existed When I joined Flowrite in June 2022, the generative AI landscape was unrecognizable from today. ChatGPT hadn’t launched. There were no LangChain tutorials, no best practices for LLM cost management, no established patterns for production AI systems. We were figuring it out as we went — and our users were growing 10x while we did it. The Early LLM Production Challenge Flowrite was Europe’s first LLM-powered email assistant. Users installed a Chrome extension, and as they composed emails, AI would suggest completions. Simple concept, complex execution. ...

The Problem with Single-Provider AI When our AI platform relied 100% on OpenAI, we experienced every problem you’d expect: Cost Volatility: One month’s bill was $15K. The next was $45K. Same features, just more usage. CFO was not happy. Availability Risk: During OpenAI’s December 2023 outages, our AI features went completely dark. Users were frustrated. Missed Opportunities: Claude’s 100K context window would have been perfect for our document analysis feature. But we couldn’t use it. ...