AI/ML
Natural Language Processing (NLP)
What NLP is and why it matters
Definition
Natural language processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and human language. NLP enables machines to read, understand, and generate text and speech. Applications include sentiment analysis, named entity recognition, machine translation, text summarization, chatbots, and document classification.
How it works
NLP is the broad field; LLMs are the current dominant approach within it. Before transformers, NLP relied on rule-based systems, statistical models, and smaller neural networks. Modern NLP is almost entirely transformer-based, which is why terms like NLP and LLM are sometimes used interchangeably - though NLP is the field and LLMs are the tool.
Common NLP tasks include sentiment analysis (is this review positive or negative?), named entity recognition (extract names, dates, and amounts from text), text classification (categorize support tickets by department), summarization (condense a 20-page report into key points), and question answering (answer user questions based on a knowledge base).
For businesses, NLP extracts value from unstructured text data - emails, support tickets, contracts, reviews, social media posts. Most organizations sit on massive amounts of text data that is only useful if it can be processed automatically. NLP turns that unstructured text into structured, useful information.
How 1Raft uses Natural Language Processing
NLP is woven into most of the AI products we build. In martech, we built a sentiment analysis pipeline that processes 100,000+ social media mentions daily and surfaces trends your team can act on. In fintech, NLP powers automated compliance document review. We use pre-trained transformer models as the foundation and add task-specific layers (classification heads, extraction prompts) tailored to each client's data.
Related terms
AI/ML
Large Language Model (LLM)
A large language model is a neural network trained on massive text datasets to understand and generate human language. LLMs power chatbots, content generation, code assistants, and most modern AI products.
AI/ML
Embeddings
Embeddings are numerical representations of data (text, images, audio) in a high-dimensional space where similar items are located near each other. They allow AI systems to measure similarity, search by meaning, and cluster related content.
AI/ML
Transformer Architecture
The transformer is the neural network architecture behind virtually all modern language models. Introduced in 2017, it uses a mechanism called self-attention to process entire sequences of text in parallel, making it far more efficient and capable than previous approaches.
AI/ML
Prompt Engineering
Prompt engineering is the practice of crafting and optimizing the instructions given to a language model to get consistent, high-quality outputs. It is the most accessible and cost-effective way to improve AI application behavior without modifying the underlying model.
AI/ML
Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation is a technique that combines a language model with a searchable knowledge base. Instead of relying solely on what the model learned during training, RAG retrieves relevant documents first, then generates answers grounded in that specific data.
AI/ML
Token (AI Context)
A token is the basic unit of text that a language model processes. Words, parts of words, and punctuation are all broken into tokens. Token counts determine model costs, context window limits, and response length constraints.
Related services
Next Step
Need help with Natural Language Processing?
We apply this in production across industries. Tell us what you are building and we will show you how it fits.