Saw a viral post on Twitter comparing "Prashant" and "Croissant" and it got me thinking — how exactly does search work? What logic is behind it?

Viral Tweet

Luckily, I follow Arpit who had already made a video breaking this down (I’ve linked that below). After watching it, I figured: why not try building something similar over the weekend? Surprisingly, it only took 2 hours because I already had a basic understanding of search algorithms.

To really get into this, I had to go deeper — here’s what I found:

🧠 Lexical Search

Lexical search is your classic, text-based search. It matches documents based on exact word matches.

How it works:

  • Words are put into an inverted index — a data structure mapping words to the documents they appear in. 
word 1 -> [doc1, doc2, doc3]
word 2 -> [doc1, doc4, doc5]

Real-world example: 

"optimization" -> ['git.pdf','codeofconduct.md','readme.md']
"CICD" -> ['azurefundamentals.pdf','readme.md']

🔴 Limitations:

  • Doesn't understand meaning or sound of words.
  • One-to-one word mapping only: 
"PullRequest" ≠ "PullRaquest"

Common issues:

  • Spelling variations: Hrushi vs Hrishi vs Rishi vs Rushi
  • Typos/misspellings: hrushikesh vs hrushkesh vs hrushiksh
  • Synonyms: car vs vehicle, automobile vs SUV
  • Abbreviations: AI vs Artificial Intelligence

🔍 Fuzzy Search

Fuzzy search handles typos or small spelling differences. It matches terms even when they aren’t exact.

Example: 

Query: Prashant
Result matches (within edit distance ≤ 4):
- merchant
- elephant
- present
- variant

Edit Distance is how many changes you need to convert one word to another.

🔊 Phonetic Search

Phonetic search matches based on how a word sounds, not how it’s spelled.

It uses algorithms like:

  • Soundex
  • Metaphone
  • NYSIIS

How it works: 

word -----------> phonetic key
"coffee" -------> KF (metaphone)

Example: 

mikaela -> MKL (Soundex)
micaela -> MKL (Soundex)

kristen -> KRSTN (Metaphone)
cristen -> KRSTN (Metaphone)

✅ Strengths:

  • Great for matching spelling variations that sound alike.
  • Fast & doesn't need training data.
  • Lookup is indexed and efficient (O(n) encoding).

⚠️ Concerns:

  • Can lead to false positives — different words that sound similar can match.

Example:

text
"file" => Soundex: F400 | Metaphone: FL
"phile" => Soundex: P400 | Metaphone: FL

🧠 Semantic Search

Semantic search goes beyond words — it captures the meaning using NLP and embeddings.

How it works:

  1. Each word/sentence is converted into a vector. 
"laptop" -> [0.25, 0.742, 0.1232, 0.5453]
  1. Perform k-NN search to find the closest vectors (similar meaning).

Pre-reqs:

  • Trained language model (like BERT, Word2Vec).
  • Corpus for training (e.g., news articles).

✅ Strengths:

  • Understands context.
  • Matches concepts not present in the corpus.

For example, your data doesn’t have "laptop" but has "notebook" — it’ll still match!

⚠️ Concerns:

  • Needs training and resources.
  • Vector comparisons are resource-intensive.
  • Approximation (ANN) may be needed for large datasets.

💡 Back to "Prashant vs Croissant"

None of the phonetic or semantic algorithms would match them — because:

prashant => Soundex: P625 | Metaphone: PRXNT | NYSIIS: PRASAD
croissant => Soundex: C625 | Metaphone: KRSNT | NYSIIS: CRASAD

They're phonetically and semantically unrelated.

So how did that tweet match them?

💭 Solution: Manual Synonym Mapping

Define a custom synonym map.

1. When query is "prashant"
2. Expand to "prashant OR croissant"
3. Fire the expanded query
4. Elasticsearch returns docs matching either

Simple and effective!

🔧 ElasticSearch Demo

I’ve created a full demo showing different types of search using AWS ElasticSearch. Check it out on GitHub.

References :
1) https://youtu.be/gZIVHSFgkvk?si=6YWgxL4PitvhZL8L
2) https://medium.com/@tzhaonj/metaphone-a-quick-and-efficient-method-for-fuzzy-name-matching-aaeb68d3c481