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GenAI Quick Answers and Series Guide
Urnish Tech·Oct 12, 2026· 3 min read
Short, plain answers to the GenAI questions we hear most: hallucination, RAG vs fine-tuning, cosine similarity, HNSW, HyDE, GraphRAG and prompt injection. Plus the full series index.
This is the starting point for our GenAI series: plain-language explanations of the ideas behind enterprise AI assistants, each with a real-world example and a working reference build. Below are quick answers to the questions we hear most, then the full series index.
Quick answers
| Question | Simple answer |
|---|---|
| What is hallucination? | The AI confidently says something false. Reduce it with RAG, citations, "say I don't know" instructions and faithfulness evals. |
| RAG vs fine-tuning? | RAG gives the model a book to read (changing knowledge). Fine-tuning trains its habits (style, format). |
| Cosine similarity? | How similar two vectors are in direction: 1 = same meaning, 0 = unrelated. |
| HNSW? | The fast search index in vector DBs. Like highways with express lanes: jump close to the target quickly, then take local roads. |
| HyDE? | The LLM writes a sample answer first, and we search with that, because answers look more like documents than questions do. |
| GraphRAG? | A knowledge graph and a vector DB together: the graph for relationships, vectors for text. |
| Prompt injection? | Hidden commands inside documents ("ignore your rules…"). Treat retrieved text as data, not instructions, and give tools minimum permissions. |
| Context bloat? | Sending too much text to the LLM: more cost, slower answers, and the important part gets lost. |
Part 1: GenAI and knowledge modeling
- Generative AI in Plain Words: What LLMs Can and Can't Do
- Taxonomy: Organize Knowledge So AI Searches Less and Finds More
- Ontology: The Blueprint Behind Enterprise Knowledge
- Knowledge Graphs: Answering the Questions Vector Search Can't
- SHACL: Validation Rules That Keep LLM-Extracted Data Honest
Part 2: Retrieval (RAG)
- RAG Explained: Give Your LLM an Open-Book Exam
- Chunking Strategies: How to Cut Documents Without Losing Meaning
- Embeddings: Google Maps Coordinates for Meaning
- Choosing an Embedding Model: Dimensions, Storage and Accuracy
- Vector Databases, Top-K and Hybrid Search
- Re-ranking: The Second Opinion That Picks the Best Chunks
Part 3: Evals and accuracy
- Golden Evals: The Answer Key Every AI Feature Needs
- Measuring AI Accuracy: Retrieval Metrics vs Answer Metrics
- Which Model for Which Job: Picking LLMs by Task, Cost and Evals
Part 4: Agentic automation
- AI Agents: From Chatbots That Talk to Assistants That Act
- Tool Calling: How LLMs Use Your APIs Without Running Code
- MCP (Model Context Protocol): The USB-C Cable for AI
- Letting AI Agents Use the Command Line, Safely
Part 5: Putting it together
We deploy these patterns across production engagements in fintech, healthcare, and eCommerce. Talk to a senior engineer about how they apply to your platform.