Concept library
1129 concepts across 20 domains and 101 tracks. Each track is a coherent sequence — read it top to bottom or dip in wherever the gap is.
All domains
01Foundations
02Transformer Internals
03Training & Fine-Tuning
04Reinforcement Learning
05Inference, Systems & Hardware
06Applied LLM Engineering
07Reasoning, Evaluation & Safety
08Multimodal & Applications
09Classical ML & Statistical Learning
10Causal Inference & Experimentation
11Time Series & Forecasting
12Graphs, Recommenders & Structured Data
13Generative Modelling Beyond Transformers
14Efficiency, Compression & Edge AI
15Search & Information Retrieval
16Data & Feature Engineering
17MLOps & Platform Engineering
18Security, Privacy & Adversarial ML
19Governance, Risk & Responsible AI
20Human-AI Interaction, Product & Economics
15
Search & Information Retrieval
Thirty years of ranking research that RAG rediscovered, usually the hard way.
5tracks
39concepts
635cards
4.7hreading
Classical Information Retrieval Inverted indexes, TF-IDF and BM25, query processing, and the lexical baselines that refuse to be beaten. 7 concepts · 141 cards
- 01 Text Analysis: Stemming, Lemmatisation and Stopwords How an analyzer chain turns raw text into index terms, what stemming, lemmatisation and stop lists trade between recall and precision, and why these decisions are fixed at indexing time.
- 02 BM25 and Term Frequency Saturation Why a term appearing twenty times should not score ten times higher than one appearing twice, how BM25 encodes that as a saturating function, and what its two parameters actually control.
- 03 Inverted Indexes and Postings Lists The data structure that makes text search sublinear in corpus size, why postings are stored as sorted document IDs, and how gap encoding turns a list of integers into a few bits each.
- 04 TF-IDF and the Vector Space Model Queries and documents as weighted term vectors ranked by cosine, why each weight multiplies local and collection-wide evidence, and why plain cosine normalisation systematically favours short documents.
- 05 Dynamic Pruning with WAND and Block-Max Retrieving the exact top-k without scoring most of the candidates, by maintaining an upper bound on what each document could score and skipping everything that cannot beat the current threshold.
- 06 Learned Sparse Retrieval Using a language model to assign weights over the vocabulary, including terms not present in the text, so semantic matching runs on an inverted index instead of a vector index.
- 07 Query Likelihood Language Models for Retrieval Ranking documents by the probability that each document's language model generates the query, and why the smoothing that makes this work quietly reintroduces IDF weighting and length normalisation.
Learning to Rank Pointwise, pairwise and listwise objectives, LambdaMART, position bias and counterfactual training. 7 concepts · 133 cards
- 01 LTR Datasets and Ranking Feature Design What the standard learning-to-rank benchmarks (LETOR, MSLR-WEB30K, Yahoo, Istella) actually contain, how their hand-built feature vectors are designed, and what their results do and do not tell you about a production ranker.
- 02 Multi-Stage Ranking Cascades Why production search and recommendation rank in stages of increasing cost over shrinking candidate sets, how to budget candidates and latency per stage, and why the first stage caps what every later stage can achieve.
- 03 Pointwise, Pairwise and Listwise Objectives Ranking is not regression, and the three families of learning-to-rank losses differ in how much of the ranking structure they put inside the objective rather than leaving to a sort.
- 04 Click Models: Cascade, DBN and UBM Generative models of how users scan and click a result page, which let a search log be turned into relevance estimates by modelling what each user probably examined and whether a click satisfied them.
- 05 Counterfactual Learning to Rank Training a ranker on logged clicks while correcting for the bias in how those clicks were generated, which turns a biased log into an unbiased estimate of a ranking objective.
- 06 LambdaRank and LambdaMART The trick of defining a gradient without ever defining a loss, which lets gradient boosting optimise a discontinuous ranking metric directly, and why the result dominated learning to rank for a decade.
- 07 Position Bias and the Examination Hypothesis Clicks measure relevance multiplied by the chance the user looked, so training on raw clicks teaches a ranker to reproduce whatever ranking generated the logs.
Query Understanding Intent classification, spelling and segmentation, expansion, rewriting, and conversational query resolution. 7 concepts · 133 cards
- 01 Query Autocompletion Proposing full queries from a typed prefix within the gap between two keystrokes, from the most-popular-completion baseline and compressed top-k tries to personalised and generative completion, and the harms of suggesting things people did not ask for.
- 02 Query Entity Recognition and Linking Finding the entity mentions in a two- or three-word query and resolving each to a knowledge-base identifier, under a latency budget measured in milliseconds and with almost no context to disambiguate from.
- 03 Query Intent and Taxonomies The same string can be three different requests, and classifying which one determines whether the right answer is a document, an entity, an action, or a generated response.
- 04 Spelling Correction and Query Segmentation Fixing what the user typed before matching it, where the hard part is not generating candidates but deciding whether the original was wrong at all.
- 05 Conversational Query Rewriting Turning a context-dependent follow-up into a self-contained query, which is what lets a stateless retriever serve a stateful conversation.
- 06 Parsing Queries into Structured Filters Turning a free-text query into attribute constraints and residual text that a structured index can execute exactly, from CRF query tagging to schema-constrained LLM parsing, and why negation is the case that makes parsing worth its cost.
- 07 Query Expansion and Pseudo-Relevance Feedback Adding terms to a query to bridge vocabulary mismatch, and the drift failure that occurs when the terms are harvested from results that were wrong to begin with.
Search Evaluation Pooling and judgments, nDCG and MRR, interleaving, online metrics, and why offline gains vanish online. 7 concepts · 129 cards
- 01 Precision, Recall and Average Precision The binary-relevance metric family that underpins ranked retrieval evaluation, from set precision and recall through P@k, R-precision and interpolated curves to average precision, and the user model each one quietly assumes.
- 02 Test Collections, Pooling and Judgment Bias The Cranfield paradigm made retrieval a measurable science, and the pooling shortcut that makes it affordable quietly penalises any system unlike the ones that built the pool.
- 03 nDCG, MRR and Graded Relevance The main ranking metrics differ in what they assume about the user, and choosing one is choosing a model of how far someone reads and what they are looking for.
- 04 Interleaving and Online Evaluation Mixing two rankers' results into a single list and attributing clicks gives a within-user paired comparison that detects differences far faster than an A/B test on the same traffic.
- 05 LLM Relevance Judgments Using large language models to label query-document relevance for offline evaluation, what the agreement numbers actually show, and the circularity that appears once the systems being judged use the same kind of model as the judge.
- 06 Significance Testing in IR Evaluation How to decide whether a MAP or nDCG difference over a few dozen topics is signal, which paired tests actually hold their error rates on IR data, and why testing many variants at once quietly inflates what counts as significant.
- 07 Why Offline Gains Vanish Online The recurring experience that an offline nDCG improvement produces no measurable online effect, and the four distinct mechanisms that cause it.
Vector Databases Index families, filtered search, freshness and deletes, sharding, and the operational reality of billion-scale ANN. 11 concepts · 99 cards
- 01 Measuring ANN Recall Honestly Why recall requires the exact search an index exists to avoid, how recall@k and 1-recall@1 differ, and why throughput reported without a recall axis is unfalsifiable.
- 02 Operating a Vector Store in Production The operational facts that decide whether a vector search system works, why memory is the binding constraint, and the migration nobody plans for.
- 03 Choosing an Index Family The four index families and the workload property that selects each, why the recall-latency-memory triangle admits no universal answer, and how to read a published benchmark.
- 04 Coarse Quantisers and the Probe Budget How an inverted-file index turns nearest-neighbour search into a cell-selection problem, why nlist is a square-root decision, and why boundary loss rather than code error sets the recall curve.
- 05 Disk-Based ANN Search and DiskANN How DiskANN serves a billion vectors from one machine with 64 GB of RAM by keeping compressed codes in memory and a Vamana graph on SSD, why the number of disk round trips is the budget that shapes every design choice, and how the Fresh and Filtered variants extend it.
- 06 Filtered Vector Search Why combining a metadata filter with a nearest-neighbour search is harder than either alone, the two naive strategies and how each fails, and what a native implementation does instead.
- 07 Freshness, Deletes and Index Maintenance Why approximate indexes are built for static data, what insertion and deletion do to their structure over time, and the segment architecture that reconciles freshness with query performance.
- 08 Navigable Small World Graphs and HNSW Construction Why greedy search on a nearest-neighbour graph gets stuck, how long-range links and a diversity-based pruning heuristic fix it, and what M, efConstruction and efSearch each actually commit you to.
- 09 Product Quantisation Codebooks and Asymmetric Distance How splitting a vector into subspaces and replacing each with a codebook index turns distance computation into table lookups, why the query is never quantised, and what shrinking the code width buys.
- 10 Sharding and Distributed Vector Search The two ways to partition a vector collection across machines, why one preserves recall and the other preserves latency, and the fan-out arithmetic that decides tail latency.
- 11 Two-Stage Search with Compressed Candidates and Exact Rescoring Why every aggressively compressed index needs a full-precision rerank pass, how k_factor converts a weak ranking requirement into a strong one, and how DiskANN turns an SSD read into the scoring stage.