Large Language Models (LLMs) didn’t appear overnight — they’re the result of decades of ideas, experiments, and breakthroughs in Natural Language Processing (NLP) , the field focused on getting computers to understand and generate human language. In many ways, NLP is the starting point for LLMs : it gave us the foundational problems (like language understanding, translation, summarization, and question answering), the evaluation methods, and — most importantly — the steady shift from hand-written rules to learning directly from data . In this blog, I’ll walk through that evolution step by step: from early rule-based systems, to statistical NLP, to neural networks, and finally to the Transformer era that made today’s internet-scale models possible. Whether you’re new to NLP or already building with modern LLMs, my goal is to make the journey feel clear, connected, and practical — so you can understand not just what changed, but why it mattered. Early Days: Rules & Statistics (1950s — 1990s) Rule-Based Systems: Initial NLP used complex, hand-written rules (if-then logic) for translation, facing limitations in handling language complexity. Georgetown-IBM Experiment (1954): The first public demo of machine translation, which translated 60+ Russian sentences into English using 6 rules and a 250-word vocabulary. ELIZA (1960s): Created by Joseph Weizenbaum, it simulated a psychotherapist using pattern matching and substitution rules (e.g., changing “I need X” to “Why do you need X?”) source: https://www.nngroup.com/articles/eliza-effect-ai/ SHRDLU (1960s): Developed by Terry Winograd, it operated within a limited “blocks world” to understand commands for moving objects, demonstrating early natural language understanding. TALE-SPIN and MYCIN Statistical Revolution: NLP went through “AI Winters” (notably 1966–1980 and 1987–1993) when funding collapsed because hand-built, rule-based systems didn’t scale to real language complexity. The breakthrough that helped end this era was Statistical NLP: in the 1980s, researchers shifted to probabilistic machine learning (e.g., n-grams), highlighted by early IBM work. These methods became foundational in NLP and Information Retrieval, representing text numerically using word frequencies and probabilities. N-gram: A statistical language model that predicts the next item in a sequence based on the previous n−1 items. https://www.educative.io/answers/what-are-n-grams BoW (Bag of Words): A model that represents text as an unordered collection of words, utilizing word frequency counts to model documents. TF-IDF: is a statistical measure used to evaluate how important a word is to a document within a collection (or corpus). It improves BoW by combining: TF (how often the word appears in the document) and IDF (how rare the word is across the corpus — common words get low IDF, rare words high). The TF‑IDF score = TF × IDF; a high score means the word is frequent in that document but uncommon overall, so it’s likely meaningful. Hidden Markov Models (HMMs) : Statistical models used to model sequential data by assuming an underlying system has unobservable (“hidden”) states that generate visible outputs. This example shows a Hidden Markov Model where the hidden states are weather conditions (Rainy, Cloudy, Sunny) and the observations are emotions (Happy, Neutral, Sad): from https://www.geeksforgeeks.org/machine-learning/hidden-markov-model-in-machine-learning/ Conditional Random Fields (CRFs) : Used for Part-of-Speech (POS) tagging where each word in a sentence is assigned a grammatical label such as noun, verb or adjective. From: https://www.geeksforgeeks.org/nlp/conditional-random-fields-crfs-for-pos-tagging-in-nlp/ Neural NLP Era(1993–2012): In the traditional ML era, progress was mostly about feature engineering + picking better algorithms — regression, SVMs, decision trees , and the like. They’re still useful, but deep learning shifted the game: instead of chasing the “perfect” algorithm, we focus on how well the model fits the data. As text data exploded, NLP’s priorities changed. Deep learning grew out of artificial neural networks (ANNs) — brain-inspired models that learn patterns from lots of data. Then CNNs excelled at extracting features (especially in vision), and RNNs pushed NLP forward by modeling sequences. Statistical NLP helped the field survive, but it eventually hit a performance ceiling: it struggled to capture long-range context and meaning. Around 2010, researchers started using simple RNNs for language modeling, and that spark helped launch the deep learning era in NLP. Recurrent Neural Networks (RNNs) & LSTMs: RNNs are designed for sequential data like time , speech , and language modeling . They keep a running “ memory ” of previous inputs using recurrent connections, which helps with short-term context . However, plain RNNs struggle with long-term dependencies mainly due to the vanishing gradient problem. source: https://www.geeksforgeeks.org/machine-learning/introduction-to-recurrent-neural-network/ To fix this, LSTMs ( Long Short-Term Memory networks ) were introduced. They’re a special kind of RNN that uses a memory cell and gating mechanisms (commonly: input, forget, output gates ) to retain information over longer periods. GRUs ( Gated Recurrent Units ) are a simplified alternative to LSTMs with fewer gates (commonly: update and reset ). They often train faster , use fewer parameters , and can be a solid choice when you have smaller datasets or limited compute . Common RNN Input–Output Patterns One-to-Many : scalar input → sequence output Example : Image captioning Many-to-One : sequence input → scalar output Example : Sentiment analysis Many-to-Many : sequence input → sequence output Asynchronous (lengths can differ): Machine translation (e.g., English → Hindi) Synchronous (same length): POS tagging, Named Entity Recognition (NER) Seq2Seq models are a specific and widely used implementation of the asynchronous many-to-many RNN architecture . Key Drawbacks of RNN-family Models Limited parallelism (they process sequences step-by-step) Long-range dependency challenges (especially in vanilla RNNs; improved but not always eliminated in variants) Prediction-Based Word Embeddings: Computers don’t understand words, only numbers. The breakthrough was prediction-based word embeddings — turning words into dense numerical vectors that capture their meaning based on how they’re used. Think of it as giving each word a unique “ meaning coordinate ” in a vast space. Word2Vec (Google, 2013) learns dense word vectors from context, capturing semantic patterns (e.g., “king − man + woman ≈ queen”). It has two main training setups: CBOW (Continuous Bag of Words) : predicts a target word from its surrounding context. Skip-gram : learns word embeddings by using a target word to predict its surrounding context words within a window. 2. GloVe builds embeddings using global co-occurrence statistics, so vectors reflect meaning based on how often words appear together across the whole corpus. 3. FastText extends Word2Vec by representing words as character n-grams, which helps with rare words and morphology (e.g., prefixes/suffixes). The Transformer Breakthrough (2013 — Present) Since 2013, NLP has entered its third stage: the large language model era . Deep learning reshaped the field by learning vector representations of words and sentences, making semantic similarity measurable and useful. After early static embeddings, NLP shifted to sequence modeling : Seq2Seq ( encoder–decoder ) → attention → Transformers . This progression unlocked scalable pre-training, efficient fine-tuning, and modern transfer learning — the foundation of today’s LLMs. Seq2Seq , introduced by Sutskever et al. (2014), used RNNs (LSTMs/GRUs) to map variable-length inputs to outputs, transforming tasks like neural machine translation. But it soon evolved: Classic Encoder-decoder: The encoder compresses the input into a single fixed-length context vector , and the decoder generates output step-by-step — effective, but weak on long sequences due to the bottleneck . source: https://ai.plainenglish.io/hello-transformers-2474e1d4a67e 2. Addition of Attention mechanism in our Traditional Encoder Decoder Architecture(2015): removes the bottleneck by letting the decoder focus on different parts of the input via all encoder states, improving long-range handling and accuracy. source: https://ai.plainenglish.io/hello-transformers-2474e1d4a67e 3. Transformers (2017, “Attention Is All You Need”) : drop RNNs entirely and use self-attention, enabling parallel processing (reading all tokens at once) and better scaling. Unlike RNNs that process tokens sequentially (slowing training and struggling with long-range dependencies), Transformers read all tokens in parallel, enabling faster training and better scaling. They also model long-range dependencies more effectively than RNNs in practice. source: https://ai.plainenglish.io/hello-transformers-2474e1d4a67e 4. Pre-trained Language Models (PLMs) & Transfer Learning: Instead of training from scratch, we take a massive PLM (pre-trained on general text) and fine-tune it for specific tasks. It allows a model to “ transfer ” its general knowledge to solve specific problems efficiently. Models like BERT ( Bidirectional ) and GPT ( Generative ) utilize the Transformer architecture to learn general language understanding from massive corpora. BERT’s architecture enabled more accurate predictions and a deeper understanding of language nuances using Bidirectional Context. GPT ( decoder-only ) trains as an autoregressive language model, making it naturally suited for fluent text generation. This was indeed the foundation of a Large Language Model. 6. Rise of LLMs (Late 2010s onwards): models with billions of parameters and massive datasets (e.g., GPT, Claude, Gemini ) delivered powerful, sometimes emergent capabilities in reasoning, coding, and multi-step instruction following.Modern LLMs (GPT-4, Claude, Gemini) From late 2018 onward, we discovered Scaling Laws: simply making the models bigger (more parameters) and feeding them more data (trillions of tokens) unlocked emergent behaviors. Unlike earlier PLMs that required fine-tuning, these modern giants can perform reasoning, coding, and multi-step instructions via prompting (zero-shot learning), often without needing any weight updates at all. Modern Developments The Open-Weight Revolution: Developers can now inspect model weights and run powerful AI locally, ensuring data privacy and reducing reliance on big tech APIs. Techniques like LoRA (Low-Rank Adaptation) enable users to fine-tune massive models on consumer hardware, leading to highly specialized “expert” models. Hybrid & Efficient Architectures: MoE (Mixture-of-Experts) : scales capacity efficiently (only some experts activate per token). RAG : combines LLMs with retrieval/search to improve factual grounding and updates. Tool use/agents : LLM calls external tools (DBs, code, calculators) for reliability and workflows. Multimodal : integrates text with vision/audio for documents, charts, UI, etc. Inference hybrids : speculative decoding (small draft + big model) to speed generation. Current Focus — Reliability & Utility: While LLMs drive complex applications, traditional NLP tools (rules, classic ML) remain essential for specialized tasks that demand speed, efficiency, and absolute precision . At the same time, active research focuses on improving reliability — reducing hallucinations , mitigating bias , strengthening safety , and making outputs more verifiable and controllable . From Rules to Transformers: The Rise of LLMs was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.