#Information Retrieval

Articles about Information Retrieval — exploring patterns, best practices, and real-world implementations in production systems.

8 posts tagged with information retrieval. ← All posts

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Pratik Dhanave · ·6 min read

Building a Search System in Practice — Putting It All Together

Knowing how inverted indexes, BM25, and vector search work is one thing; assembling them into a search system that's fast, relevant, and maintainable is another. This closing post is the practical synthesis — the architecture of a real search system, the choices that matter, and the hard-won operational lessons that no algorithm paper mentions. It turns the series into something you can actually build, whether it's a product search box or the retrieval layer of a RAG application.

Knowing the algorithms is one thing; assembling a fast, relevant, maintainable search system is another. The anatomy (offline index / online retrieve-then-rank), the decisions that matter (buy vs build, keyword/semantic/hybrid), continuous relevance measurement, and why the retriever caps a RAG system's quality.

#Information Retrieval#Search Systems#RAG#Engineering#Search
Pratik Dhanave · ·6 min read

Semantic and Hybrid Search — Matching by Meaning

Keyword search has one incurable weakness: it matches words, not meaning, so it misses relevant results that use different vocabulary than the query. Semantic search fixes this by representing queries and documents as vectors of meaning and matching by proximity in that space — the technology behind modern RAG. But the surprising lesson of production search is that semantic didn't replace keyword; the best systems combine them. This post explains vector search and why hybrid retrieval wins.

Keyword search matches words, not meaning. Semantic search embeds queries and documents into a vector space and matches by proximity — the engine of RAG — but it's weak at exact matches. Why keyword and semantic have opposite strengths, and why hybrid retrieval (not replacement) is the modern default.

#Information Retrieval#Semantic Search#Hybrid Search#RAG#Search
Pratik Dhanave · ·6 min read

Learning to Rank — When Machine Learning Takes Over Relevance

BM25 ranks by a fixed formula, but real search engines have hundreds of relevance signals — freshness, popularity, personalization, link quality, and dozens of text features — and no human can hand-tune how to weigh them all. Learning to rank hands that job to machine learning: train a model on examples of good and bad rankings, and let it discover the optimal combination of signals. This post is where search ranking becomes a machine-learning problem, and the bridge to the neural retrieval behind modern AI.

Real engines have hundreds of relevance signals no human can hand-weight — so learning to rank trains a model on good/bad rankings. Pointwise/pairwise/listwise framings, gradient-boosted rankers, and the bridge to neural ranking, embedding retrieval, and the rerankers behind modern RAG.

#Information Retrieval#Learning to Rank#Machine Learning#Neural IR#Search#RAG#Embeddings
Pratik Dhanave · ·6 min read

Evaluating Search — How Do You Know Your Results Are Good?

You changed the ranking function and the results look different — but are they better? Search quality feels subjective, yet the entire field rests on measuring it rigorously, because you cannot improve what you cannot measure. Evaluating search means quantifying relevance with metrics that respect what users actually care about: not just whether the right documents appear, but whether they appear near the top. This post is about measuring relevance honestly, offline and online.

You changed the ranking and results look different — but are they better? Precision and recall ignore order (fatal for a ranked list), so rank-aware metrics (precision@k, MRR, NDCG) rule. Offline test collections for fast iteration, online A/B tests as ground truth — the same metrics that evaluate RAG retrieval.

#Information Retrieval#Evaluation#NDCG#Search Quality#Search#RAG#Testing
Pratik Dhanave · ·6 min read

Ranking — TF-IDF and BM25, the Math of Relevance

Finding the documents that contain your words is the easy part; putting the best one first is where search lives or dies. For decades, the answer to "how relevant is this document to this query?" has come from two intuitions made precise: a word matters more in a document that uses it often, and matters less if it's common across all documents. TF-IDF and BM25 turn those intuitions into the scoring functions that rank most of the world's text search — and understanding them demystifies why search returns what it does.

Finding matches is easy; putting the best first is where search lives. Two intuitions — a word matters more when frequent in a document, less when common across all — become TF-IDF and then BM25, the decades-long default (and the keyword score in hybrid RAG). Its lexical ceiling is the vocabulary gap.

#Information Retrieval#BM25#TF-IDF#Ranking#Search#RAG
Pratik Dhanave · ·5 min read

Text Analysis — Turning Messy Text into Searchable Terms

Before a single word goes into the index, it passes through a quiet, decisive step: text analysis. Deciding what counts as a "word," how to normalize it, and which words to keep is what lets a search for "running" find a document about "ran," and what makes "The" not clog the index. Get analysis right and search feels intelligent; get it wrong and no clever ranking can save it. This post is about the unglamorous preprocessing that quietly determines search quality.

Before a word hits the index it passes through text analysis: tokenization (what is a word?), normalization (case folding, stemming vs lemmatization), stop words, and synonyms. The unglamorous step where a huge share of search quality is won — and where query and documents must speak the same vocabulary.

#Information Retrieval#Text Analysis#Tokenization#Search#RAG
Pratik Dhanave · ·6 min read

The Inverted Index — The Data Structure That Makes Search Possible

One data structure is responsible for the fact that you can search billions of documents in milliseconds: the inverted index. It's a beautifully simple idea — instead of asking "what words are in this document?", precompute "what documents contain this word?" — and it's the engine inside every search engine from Google to Elasticsearch. This post builds the inverted index from scratch and shows why it, and not raw scanning, is how search actually works.

One structure lets you search billions of documents in milliseconds: the inverted index. Instead of 'what words are in this document?', precompute 'what documents contain this word?'. Postings lists, intersecting them for queries, and why it scales to the web — and underlies the keyword half of hybrid RAG.

#Information Retrieval#Inverted Index#Search#Data Structures#RAG
Pratik Dhanave · ·5 min read

The Retrieval Problem — Finding the Needle in Billions of Haystacks

Every time you type into a search box and get useful results in milliseconds from billions of documents, a remarkable amount of engineering is hiding behind the cursor. Information retrieval is the science of finding relevant information in a large collection, and it's one of the oldest, most practical, and — in the age of RAG — most suddenly-relevant-again fields in computing. This series builds search from first principles, and it starts with why "just find the matching documents" is far harder than it sounds.

Finding relevant information in a huge collection in milliseconds is far harder than 'just find the matching documents'. The two problems hiding in search (matching and ranking), why scanning fails, and the offline-index/online-retrieve pipeline that makes search fast — and that RAG reuses directly.

#Information Retrieval#Search#Fundamentals#RAG

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.