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Generative AI and LLMs
Transformers and Language Models Explained for Product Teams
Transformers power today’s language models, from search ranking to chat assistants. This guide explains the key ideas — attention, tokens, context windows, model types and training stages — in plain language, and what they mean for cost, quality and product design.
Key takeaways
- The transformer architecture, introduced in the 2017 paper “Attention Is All You Need”, uses attention to relate every token in a sequence to every other, enabling parallel training at scale.
- Language models predict text one token at a time; large models trained on vast data can follow instructions, summarise, translate and write code.
- Encoder models suit understanding tasks such as classification and search; decoder models suit generation; encoder-decoder models suit translation and summarisation.
- Context windows, token costs, latency and hallucination are the practical constraints product teams must design around.
- Most products adapt pre-trained models with prompting and retrieval; fine-tuning is used for specific styles or tasks.
On this page
Language models now appear in search boxes, support tools, code editors and document workflows. Product teams do not need to understand the mathematics, but understanding a few core ideas helps make better decisions about cost, quality and design.
From sequences to attention
Earlier language models processed text word by word, which made long-range relationships hard to learn and training slow. The 2017 paper “Attention Is All You Need” by Vaswani and colleagues introduced the transformer, which uses an attention mechanism: for each token, the model learns how much to “attend” to every other token in the sequence. This captures long-range relationships and allows training to run in parallel on modern hardware, which made today’s very large models possible.
Key concepts
| Concept | Plain-language meaning | Why it matters for products |
|---|---|---|
| — | — | — |
| Token | A chunk of text — a word, part of a word or punctuation | Pricing, limits and latency are measured in tokens |
| Context window | How many tokens the model can consider at once | Limits how much document or history you can include |
| Pre-training | Learning general language patterns from large datasets | Gives broad capability out of the box |
| Instruction tuning | Further training to follow instructions helpfully | Makes models usable as assistants |
| Fine-tuning | Training on your examples for a specific task or style | Improves consistency for narrow tasks |
| Embeddings | Numerical representations of meaning | Power semantic search and retrieval |
| Temperature | Controls randomness of outputs | Lower for consistency, higher for creativity |
Most product decisions involve tokens, context and adaptation method.
Types of transformer models
| Type | Strengths | Examples of use |
|---|---|---|
| — | — | — |
| Encoder-only | Understanding text: classification, entity extraction, embeddings | Search ranking, intent detection, document classification |
| Decoder-only | Generating text | Chat assistants, drafting, code generation |
| Encoder-decoder | Transforming one text into another | Translation, summarisation |
Large general-purpose assistants are usually decoder-only models.
Strengths and limits
Strengths: flexible language understanding, generation, summarisation, translation, code, and reasoning across many domains with little task-specific data.
Limits:
- Hallucination: confident but incorrect statements, especially about facts not in the context.
- Context limits: information outside the context window is unavailable; very long contexts can reduce attention to details.
- Cost and latency: large models are slower and more expensive per request.
- Sensitivity to wording: outputs can change with small prompt changes.
- Security: vulnerable to prompt injection and data leakage without safeguards.
What this means for product design
- Ground answers with retrieval from your content rather than relying on what the model memorised.
- Design the context carefully — see our guide to context engineering.
- Choose model size by task: smaller models for classification and extraction, larger ones for complex reasoning.
- Evaluate systematically on your own examples.
- Constrain outputs with structured formats and validation.
- Plan for cost by estimating tokens per task and volume.
Choosing a model for a task
| Task | Typical choice | Considerations |
|---|---|---|
| — | — | — |
| Classification, intent detection | Small encoder or small generative model | Fast and cheap; fine-tune if many examples |
| Semantic search | Embedding model | Quality of retrieval matters more than size |
| Extraction to structured fields | Small to medium generative model with schema | Validate outputs |
| Summarisation of long documents | Medium to large model with adequate context | Check for omissions |
| Complex reasoning, agents, code | Large frontier models | Higher cost and latency; strong evaluation |
| On-device or offline | Small open models | Lower capability; privacy and latency benefits |
Route tasks to the smallest model that meets quality targets.
Adapting models: prompting, retrieval, fine-tuning
| Approach | Use when |
|---|---|
| — | — |
| Prompting | The task is clear and general knowledge suffices |
| Retrieval-augmented generation | Answers must reflect your documents and data |
| Fine-tuning | You need consistent style, format or behaviour on a narrow task with many examples |
Our guide to RAG or fine-tuning compares these in more depth.
Frequently asked questions
Do we need to train our own language model?
Almost never. Pre-trained models adapted with prompting, retrieval and sometimes fine-tuning meet most business needs.
Why do models make things up?
They generate likely text rather than looking up facts. Grounding with retrieved sources and checking outputs reduces this.
Are larger context windows always better?
They allow more information, but cost more and models may miss details in very long inputs. Retrieval of relevant passages is often better.
What is the difference between a transformer and a large language model?
The transformer is the architecture; a large language model is a specific model built with it and trained on large amounts of text.
Sources and methodology
Facts in this article were checked against the sources below on 6 November 2026. Directory data (ratings, review counts, rate bands and team sizes) changes over time and is shown as published on the date accessed. Illustrative examples are labelled as such and are not quotes.
- Vaswani et al.: Attention Is All You Need (arXiv, 2017) — accessed 6 November 2026. Introduced the transformer architecture based entirely on attention
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