-
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
-
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 ?
-
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
-
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 ?
-
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
-
GenAI – You Are Facing High Latency In RAG Pipeline What Are The Steps You Will Follow To Solve This ?
GenAI – How To Solve Latency In RAG Pipeline ? Table Of Contents: Break Down the Pipeline Components Measure and Profile Latency per Component Query Embedding Generation Time Vector Retrieval / Vector Database Time Reranking (if used) Time LLM Inference Time Prompt Construction Time Network / System-Level Issues Time Parallelize Where Possible Tools & Techniques (1) Breakdown The Pipeline Component (2) Measure And Profile Latency Per Component. (3) Query Input Component Solution: (4) Query Preprocessing & Embedding Component (5) Vector Search Component (6) Vector Search Component (7) Prompt Construction Component (8) LLM Inference Component (9) Post Processing Component (10) Caching/Storage
-
GenAI – Scenario Based Q & A
-
GenAI – Approximate Nearest Neighbors (ANN)
GenAI – Approximate Nearest Neighbors (ANN) Table Of Contents: Foundational Concepts What is Nearest Neighbor Search (NNS)? Exact vs Approximate Nearest Neighbors Trade-offs: Speed vs Accuracy vs Memory Use cases in GenAI: Semantic Search, RAG, Recommendation Systems Distance Metrics Euclidean Distance Cosine Similarity Manhattan (L1) Distance Dot Product Similarity Choosing the right metric based on data and task Core ANN Algorithms & Techniques Locality-Sensitive Hashing (LSH) Concept and hash function families MinHash, SimHash Hierarchical Navigable Small World Graphs (HNSW) Graph-based ANN Navigation and hierarchy Product Quantization (PQ) Vector compression for large-scale retrieval IVF (Inverted File Index) + PQ Clustering +
-
GenAI – Creative Co-Pilot Tools
