"A search engine that only knows words is reading the letter of the question. One that understands embeddings is reading its intent."
Key Takeaways
- Understand why traditional keyword search struggles with natural language.
- Learn what semantic search is and how it differs from keyword search.
- Understand what AI embeddings are and how they represent meaning.
- See how similarity is calculated between pieces of text.
- Learn how chunking, vector databases, and RAG fit together.
- Understand hybrid search and when to use it.
- Explore real-world examples across HR, e-commerce, and support systems.
- Avoid common mistakes when building a semantic search system.
Index
- Why Keyword Search Falls Short
- What Is Semantic Search?
- What Are AI Embeddings?
- Visualizing Embeddings
- How Semantic Search Works, Step by Step
- Measuring Similarity
- Embeddings Beyond Text
- Vector Databases
- Why Chunking Matters
- Semantic Search in a RAG Pipeline
- Semantic Search vs Keyword Search
- Hybrid Search
- Real-World Examples
- Choosing an Embedding Model
- Common Mistakes
- Semantic Search Is Not the Same as an LLM
- Interesting Facts & Stats
- FAQs
- Conclusion
1. Why Keyword Search Falls Short
Search has traditionally been built around keywords. Type "best laptop for programming" into a keyword-based engine, and it looks for pages containing words like laptop, programming, best, developer, or coding.
But what happens when a user describes the same need using entirely different words?
Example:
A company policy document says:
"Employees working remotely can claim reimbursement for internet expenses up to ₹1,500 per month."
A user searches:
"Can I get money for my Wi-Fi bill while working from home?"
A keyword engine may fail here. The query uses Wi-Fi bill and working from home; the document uses internet expenses and remote work. The words don't match - even though the meaning clearly does.
This is exactly the gap semantic search is built to close.
How Keyword Search Works, Simplified
User Query
↓
Extract Keywords
↓
Find Matching Words
↓
Rank Results
↓
Return Documents
This approach is fast and excellent for exact matches (product codes, order IDs, names). But it has no real understanding of language - it only knows whether words occur, not what they mean.
Common failure patterns:
Humans instantly see these as the same question. A keyword index does not.
2. What Is Semantic Search?
Semantic search is a technique that tries to understand the meaning and intent behind a query, rather than relying only on exact word matches.
Example:
- Query: "Can employees work from home?"
- Document: "Staff members may perform their duties remotely up to three days per week."
The vocabulary is completely different, but the meaning is closely related - and semantic search can detect that.
Simplified Architecture
User Query
↓
Embedding Model
↓
Query Vector
↓
Vector Database
↓
Similarity Calculation
↓
Most Relevant Results
The core component that makes this possible is the embedding model.
3. What Are AI Embeddings?
An embedding is a numerical representation of data - usually text - that captures the meaning of that data as a list of numbers (a vector).
Instead of representing a sentence as plain words:
"How can I reset my password?"
an embedding model converts it into a vector:
[0.021, -0.184, 0.732, 0.091, ...]
Real embedding vectors typically contain hundreds or thousands of dimensions. You're not meant to interpret each number individually - together, they encode the semantic characteristics of the text.
Two sentences with similar meaning produce similar vectors:
"How can I reset my password?" → [0.21, -0.13, 0.74, ...]
"I forgot my login password." → [0.19, -0.11, 0.71, ...]
Because the meanings are close, the vectors end up close together in space.
4. Visualizing Embeddings
"Every embedding is a small act of translation - turning human meaning into something math can compare."
A simple way to picture this: imagine every sentence as a point on a huge map, where related meanings cluster near each other.
Programming
●
●
Coding
●
Database ●
Cooking
●
● Recipe
Real embedding space has hundreds or thousands of dimensions, so this is only a simplified mental model - but the underlying idea holds:
Similar meanings occupy nearby regions in vector space.
For example:
"How do I reset my password?"
●
/ \
/ \
● ●
"I forgot my password" "I can't log in"
An unrelated sentence like "The restaurant closes at 11 PM" would sit far away from this cluster.
5. How Semantic Search Works, Step by Step
A typical semantic search system has four core stages.
Step 1 - Convert Documents Into Embeddings
Suppose a knowledge base has three documents:
- "Employees can work remotely three days per week."
- "Employees receive 20 days of annual leave."
- "Employees can claim internet reimbursement up to ₹1,500."
Each document is passed through an embedding model and converted into a vector, which is then stored in a vector database.
Step 2 - Convert the User Query Into an Embedding
The user asks:
"Can I get Wi-Fi expenses reimbursed when working from home?"
The same embedding model converts this query into a vector, capturing its meaning in the same numerical space as the documents.
Step 3 - Calculate Similarity
Document 3 is the strongest match.
Step 4 - Return the Most Relevant Results
Even though the document never uses the phrase "Wi-Fi expenses," the semantic relationship is strong enough for the system to surface it as the top result.
6. Measuring Similarity
"Keyword search finds what you typed. Semantic search finds what you meant."
The most common method for comparing embeddings is cosine similarity, which measures how closely two vectors point in the same direction.
General interpretation:
(Exact thresholds vary by embedding model and dataset.)
Simple example:
- Query vector: A = [0.8, 0.6]
- Document vector: B = [0.7, 0.7]
These point in nearly the same direction, so their similarity score is high.
An unrelated document vector, e.g. C = [-0.8, 0.2], points in a very different direction - producing a much lower similarity score.
7. Embeddings Beyond Text
Embeddings aren't limited to sentences. They can represent almost any type of content:
- Text - articles, documentation, FAQs, product descriptions, support tickets
- Images - visual characteristics captured as a vector
- Audio - speech or sound represented numerically
- Code - relationships between functions, patterns, and programming concepts
This versatility is why embeddings have become a core building block across modern AI applications, not just search.
8. Vector Databases
Once documents are converted into vectors, they need a place built for fast similarity search at scale - this is what a vector database does.
Popular options include:
- Pinecone
- Qdrant
- Weaviate
- Milvus
- pgvector (an extension for PostgreSQL)
A typical stored record looks like this:
{
"id": 102,
"text": "Employees working remotely can claim internet expenses...",
"embedding": [0.12, -0.31, 0.72, ...]
}
When a user searches, the system generates a query vector and asks the database to find the most similar stored vectors - efficiently, even across millions of records.
9. Why Chunking Matters
Imagine a 50-page employee handbook. You could generate one giant embedding for the whole document - but that creates a problem.
If a user asks "How much internet reimbursement can I claim?" and the answer is buried on page 37, a single document-wide vector will blur that specific detail with everything else in the handbook.
Instead, documents are split into smaller chunks, each with its own embedding:
Document
↓
Chunk 1 → Vector 1
Chunk 2 → Vector 2
Chunk 3 → Vector 3
...
Chunk 100 → Vector 100
This lets the search system retrieve the specific section that actually answers the question, instead of a diluted whole-document match.
10. Semantic Search in a RAG Pipeline
Semantic search is a foundational piece of Retrieval-Augmented Generation (RAG).
Suppose a company builds an AI assistant and uploads:
- employee-handbook.pdf
- leave-policy.pdf
- remote-work-policy.pdf
- insurance-policy.pdf
Indexing Phase
Documents
↓
Extract Text
↓
Split Into Chunks
↓
Generate Embeddings
↓
Store in Vector Database
Query Phase
An employee asks: "How many days can I work remotely?"
User Question
↓
Generate Query Embedding
↓
Search Vector Database
↓
Retrieve Relevant Chunks
↓
Send Chunks + Question to LLM
↓
Generate Answer
The LLM doesn't search the whole company database itself - the retrieval system finds the relevant chunks first, and the LLM uses them to compose a grounded answer, e.g.:
"According to the remote work policy, employees can work remotely up to three days per week with manager approval."
11. Semantic Search vs Keyword Search
Neither approach is universally better - which is why most production systems combine the two.
12. Hybrid Search
Hybrid search blends keyword and semantic scoring into a single ranking:
Keyword Search + Semantic Search → Combined Ranking → Better Results
Example: Searching "ORD-10452" - an exact order ID - is a job for keyword search. Semantic search adds little value here.
But for a query like "How can I cancel an order that hasn't shipped yet?", semantic search does the heavy lifting.
A hybrid system might combine both with a weighted formula:
Final Score = 0.4 × Keyword Score + 0.6 × Semantic Score
The ideal weighting depends entirely on the application and its query patterns.
13. Real-World Examples
HR Knowledge Assistant
Given three policy documents (Leave, Remote Work, Internet Reimbursement), an employee asks:
"Does the company pay for my home internet?"
Keyword search struggles because the policy says "internet expenses," not "home internet." Semantic search resolves this easily:
Internet Policy → 0.91
Remote Work Policy → 0.73
Leave Policy → 0.18
The system correctly retrieves the internet reimbursement policy.
E-Commerce Product Search
Products:
- Apple MacBook Air M4, 16GB RAM, 512GB SSD
- Dell XPS 13, Intel Core Ultra, 16GB RAM
- Lenovo ThinkPad, 32GB RAM, 1TB SSD
Search query: "lightweight laptop for programming with lots of memory"
None of the listings contain the word "programming." Semantic search bridges the gap by recognizing conceptual relationships:
programming → developer → coding → software development
lots of memory → high RAM → 16GB / 32GB RAM
This lets the system surface relevant laptops based on meaning, not exact wording.
14. Choosing an Embedding Model
Not all embedding models perform equally - they differ in quality, dimensionality, language support, cost, and speed. Common options include:
- OpenAI embedding models
- Cohere embedding models
- Voyage embedding models
- Sentence Transformers
- BGE family of models
- Nomic embedding models (including local options like nomic-embed-text)
The embedding model you choose directly determines your search quality - it's worth evaluating rather than defaulting to the first option.
15. Common Mistakes
- Chunks that are too large - dilutes retrieval precision.
- Chunks that are too small - loses important context.
- Ignoring metadata - similar content from different projects/users can bleed together without proper filtering.
- Relying only on vector similarity - semantic closeness isn't the same as factual relevance.
- Assuming top similarity = correct answer - a high score is not a guarantee of accuracy; retrieval still needs evaluation.
A robust production system typically combines:
Vector Search + Keyword Search + Metadata Filtering + Reranking
16. Semantic Search Is Not the Same as an LLM
This distinction matters:
These pieces often work together, but they solve different problems. In fact, you can build a fully functional semantic search engine that simply returns relevant documents - with no LLM involved at all.
17. Interesting Facts & Stats
- The global vector database market was valued around $2.6-2.7 billion in 2025 and is projected to reach $8.9-10.6 billion by 2030-2032, growing at a CAGR of roughly 24-27%. - MarketsAndMarkets
- 67% of surveyed engineering organizations already use a vector database in production, and 76% say adoption is more than experimental - a striking penetration rate for a technology barely known five years ago. - HostingAdvice, Sept 2025
- 9 in 10 engineers manage more than 1 million vectors in production, and nearly half handle between 10-100 million vectors. - HostingAdvice
- Google Trends searches for "vector database" grew 11× between January 2023 and January 2025. - DataAspirant
- The open-source vector database Milvus has over 44,000 GitHub stars, making it one of the most starred projects in the space. - Firecrawl
- The MMTEB multilingual benchmark now spans 1,038 languages across 131 datasets, showing how far semantic search has scaled beyond English-only systems. - Typedef.ai
- Embedding dimensionality varies wildly by design goal: Google's Gemini Embedding uses 3,072 dimensions for maximum expressiveness, while Multilingual-e5-small uses just 384 dimensions for speed and efficiency - a direct trade-off between accuracy and latency. - Typedef.ai
- Some production embedding pipelines now achieve total query latency around 16 milliseconds, fast enough for real-time, interactive semantic search. - Typedef.ai / AIMultiple Research
- In SEO and content discovery, about 47% of marketers already use AI-driven semantic optimization tools, and 84% use AI tools for trend analysis - evidence that "matching meaning" has moved from search infra into marketing workflows. - BrightEdge via Foresight Fox
- North America leads enterprise semantic search adoption at 65%+ among large enterprises, driven by mature data infrastructure and early AI investment. - Salfati Group
18. FAQs
Is semantic search always better than keyword search? No. For exact identifiers (order numbers, SKUs, names), keyword search is usually faster and more accurate. Semantic search shines with natural-language, conversational queries.
Do I need a vector database to do semantic search? For small datasets, in-memory similarity search may be enough. At scale, a dedicated vector database (Pinecone, Qdrant, Weaviate, pgvector, etc.) becomes necessary for performance.
What's the ideal chunk size? There's no universal number - it depends on your content and use case. Too large loses precision; too small loses context. Most teams tune this empirically.
Can semantic search work without an LLM? Yes. Embeddings, similarity search, and ranking can operate entirely on their own to return relevant documents.
Is a high similarity score proof of a correct answer? No. It indicates conceptual closeness, not factual correctness - which is why evaluation and sometimes reranking are still necessary.
19. Conclusion
Traditional search asks: "Which documents contain these words?" Semantic search asks: "Which documents are closest in meaning to what the user is asking?"
That shift changes everything. A query like "How do I get my money back for a flight I cancelled?" can be understood as connected to refund, cancellation, and reimbursement - even when none of those exact words appear in the query itself.
The Full Flow, Summarized
Documents
↓
Chunking
↓
Embedding Model
↓
Vectors
↓
Vector Database
↓
User Query
↓
Query Embedding
↓
Similarity Search
↓
Relevant Results
↓
Optional LLM
↓
Natural-Language Answer
Keyword search remains valuable - especially where exact matching matters. But when users express the same idea in countless different ways, semantic search delivers a far more intelligent experience.
Keywords provide precision. Embeddings provide understanding.
And when semantic retrieval is combined with an LLM through RAG, applications move beyond simply finding words - toward genuinely finding knowledge.
About the Author:Vatsal is a web developer at AddWebSolution. Building web magic with Laravel, PHP, MySQL, Vue.js & more.
