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Technical Report: Modern RAG Architectures

Noosphere (SmartFAQs Originals)

Explorations in Structured AI Reasoning (Article Set)

I. Foundational Concepts

Benefits Over Standard LLM Generation

Why RAG Reduces Hallucinations

RAG vs. Prompting vs. Fine-tuning Comparison

Historical Evolution of RAG

Core RAG Elements

RAG Architecture Taxonomy

Model Selection Strategies

II. Retrieval Systems & Techniques

Chunking Strategies

Search Algorithms

Structured & Semantic Search

Retriever & Generator Co‑Design

III. RAG Pipelines & Patterns

Basic RAG Flows

Advanced RAG Patterns

Agentic & Dynamic Strategies

Pipeline Extensions

Recursive Language Models

IV. Use Cases & Applications

Customer Support Agents

Legal Assistants

Medical & Clinical Decision Aids

Enterprise Knowledge Bases

Document Search & Summarization

Content Automation

Research Assistants

Adaptive Learning Systems

Journalism & Fact Checking

Code Assistants

API Documentation Search

DevOps Knowledge Retrieval

Specification Summarizers

Personal Knowledge Assistants

Educational Tutors

Personal Knowledge Management (PKM)

V. Data Ingestion & ETL for RAG

Transformation & Cleaning

Storage & Formats

Metadata Enrichment

VI. Vector Databases & Platforms

Commercial & Managed Options

Self-Hosted & Open Source

Metadata & Filtering

VII. Evaluation, Metrics & QA

Benchmarks & Tools

VIII. Production & Deployment

Security & Compliance

IX. Advanced Extensions

Multi-Modal RAG

Dynamic Knowledge Bases

Personalization

X. Tools, Libraries & Ecosystem

Open Source SDKs

Connector Tools

Orchestration Platforms

Monitoring & Observability

2025 Plug‑Ins & API Ecosystem

XI. Prompts, Templates & Best Practices

Prompting for RAG

Dynamic Prompting

Prompt Evaluation

XII. Governance & Ethical Considerations

Bias Mitigation

Data Provenance Tracking

Consent & Privacy Policies

XIII. Emerging Trends & Research

Graph-RAG Hybrids

Retrieval-Based Fine-Tuning (RAFT)

Cross-Lingual RAG

Zero-Shot vs Few-Shot RAG

RAG + Simulation / Synthetic Context

Causal & Structured Retrieval

Continual Learning with RAG

XIV. Real‑World Case Studies & Benchmarks

XV Visual Topical Representations

Mermaid Diagram of RAG Topology

XVI. AI Agents & Chatbots

Orientation: Chatbots vs AI Agents

Core Foundations: What Makes an AI Agent

Cognitive Architectures & Reasoning Strategies

Agent Design Patterns

Frameworks, Tooling, and Runtimes

Operations, Ethics, and Failure Modes

Practical Deployment Playbooks

The Future of Agents

XVII Additional Strategic Considerations

Performance Optimization Trade-offs

Scalability Pathways

Team Structure for RAG Development

Common Failure Patterns

Best Practices Summary

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