Praudyog

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  • GenAI – Road Map To Learn GenAI.

    GenAI – Roadmap To Learn GenAI. Table Of Contents: Introduction To GenAI. Tokenization & Embedding. Chunking & Types Of Chunking. Vector Indexing. Vector Reranking. Prompt Engineering. Large Language Models(LLMs) Fine Tuning & Adaption Technique. Retrieval Augmented Generation(RAG). Evaluation Metrics. Model Debugging & Troubleshooting.  System Design & Architecture. Deployment, MLOPS & Scalability. Security, Guardrails & Governance. Monitoring & Observability. User Experience & Customization. Multimodal GenAI. Tools, Frameworks & Libraries. Use Cases & Industries Application.  Scenario Based Q & A.

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  • GenAI – How To Optimize Monitoring & Observability Process ?

    GenAI – How To Optimize Monitoring & Observability Process ? Table Of Contents: What Is Monitoring & Observability ? How Monitoring & Observability Adds Latency In RAG Pipeline ? How To Optimize Monitoring & Observability Process ? (1) What Is Monitoring & Observability? (2) How Monitoring & Observability Adds Latency In RAG Pipeline ? (3) How To Optimize Monitoring & Observability Process

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  • GenAI – How To Optimize Logging & Catching Process ?

    GenAI – How To Optimize Logging & Catching Process Table Of Contents: What Is Postprocessing ? How Postprocessing Adds Latency In RAG Pipeline ? How To Optimize Postprocessing Step? (1) What Is Postprocessing In RAG ? (2) What Is Postprocessing In RAG ? (3) How to Optimize Logging & Caching Process

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  • GenAI – How To Optimize Postprocessing Steps ?

    GenAI – How To Optimize Postprocessing Steps ? Table Of Contents: What Is Postprocessing ? How Postprocessing Adds Latency In RAG Pipeline ? How To Optimize Postprocessing Step? (1) What Is Postprocessing In RAG ? (2) How Postprocessing Adds Latency In RAG Pipeline (3) How to Optimize Postprocessing Step

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  • GenAI – How To Optimize LLM Inference Process ?

    GenAI – How To Optimize LLM Inference Process ? Table Of Contents: What Is LLM Inference Step ? How LLM Inference Step Add Latency In RAG Pipeline ? How To Optimize LLM Inference Process ? (1) What Is LLM Inference Step ? (2) How LLM Inference Step Add Latency In RAG Pipeline ? (3) How to Optimize LLM Inference Step

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  • GenAI – How To Optimize Prompt Construction Process ?

    GenAI – How To Optimize Prompt Construction Process Table Of Contents: What Is Prompt Construction Process ? How It Can Add Latency In RAG Pipeline ? How To Optimize Prompt Construction Process ? (1) What Is Prompt Construction Process ? (2) How Prompt Construction Adds Latency ? (3) How To Optimize Prompt Construction Process ?

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  • GenAI – How To Optimize Vector Reranking Process ?

    GenAI – How To Optimize Vector Reranking Process ? Table Of Contents: What Is Vector Reranking ? How Vector Reranking Adds Latency ? How To Optimize Vector Reranking Process ? (1) What Is Vector Reranking ? (2) How Vector Reranking Adds Latency? (3) How to Optimize Vector Reranking in RAG

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  • GenAI – How To Optimize The Vector Retrieval Process ?

    GenAI – How To Optimize Vector Retrieval Process ? Table Of Contents: What Is The Vector Retrieval Process ? How It Can Add Latency In The RAG Pipeline ? How To Reduce Latency Due To Vector Retrieval ? (1) What Is Vector Retrieval Process ? (2) How Vector Retrieval Adds Latency ? (3) How Optimize Vector Retrieval Latency ?

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  • GenAI – How To Optimize Query Preprocessing & Embedding Component ?

    GenAI – How To Optimize Query Preprocessing & Embedding Component ? Table Of Contents: What Is Query Preprocessing & Embedding Layer. Where Can Latency Happen ? How To Reduce Latency (1) What Is Query Preprocessing & Embedding Layer ? (2) How Text Preprocessing Can Add Latency In The Process? What Is Compiled Regex ? Example-1: import re # Compile The Regex Pattern Once. pattern = re.compile(r'W+') #Use The Compiled pattern clean_text = pattern.sub(' '."This is @ a sample # text") print(clean_text) This is a sample text Example-2: import re non_alpha_pattern = re.compile(r'[^a-zA-Zs') def preprocess_text(): text = text.lower() text = non_alpha_pattern.sub(''

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  • GenAI – How To Optimize User Query Component ?

    GenAI – How To Optimize User Query Component ? Table Of Contents: What Is Query Input Component? Network Optimization Techniques. Use HTTP/2 or gRPC Compress Payloads Avoid Cold Start Problem (1) What Is Query Input Component ? (2) Network Optimization Techniques. (3) Use HTTP/2 or gRPC (4) Compress Payloads Use Compression (gzip or Brotli) import gzip import requests query = { “user_query”:”…” # a very large string } #Compress JSON compressed_data = gzip.compress(bytes(str(query), ‘utf-8’)) headers = { “Content-Encoding”: “gzip”, “Content-Type”: “application/json” } response = request.post(“http://localhost:8000/rag/query”, data=compressed_data, headers=headers) Use Decompression (gzip or Brotli) from fastapi import FastAPI, Request import gzip import

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