How transformers work in AI: next tokens, positions, GPT, encoders and long inputs

Transformers are the neural network architecture behind modern language models such as GPT, Claude and Gemini. Introduced in the 2017 paper Attention Is All You Need, they use self-attention to relate every token to every other token in parallel. I fine-tune transformer models with QLoRA in FineTune Studio, so this page covers what transformers are, how LLMs work, next-token prediction, positional encoding, why GPT is a transformer, encoders and decoders, input limits, long sequences, gibberish output and workshop ideas.

What are transformers in AI?

Transformers are a neural network architecture that processes sequences, such as text, by using self-attention to weigh how much each token relates to every other token. They power large language models, translation, speech recognition and vision models.

Before transformers, recurrent networks processed text one word at a time, which was slow and struggled with long-range connections. Transformers process all tokens in parallel, making training on huge datasets efficient on GPUs. A transformer stacks layers, each with multi-head attention and a feed-forward network, plus residual connections and normalisation. The same design now handles images in vision transformers, audio in Whisper and protein structures, making it the most influential architecture in modern AI.

How do LLM transformers work?

An LLM transformer converts text into tokens, maps each token to an embedding vector, passes the vectors through many layers of self-attention and feed-forward networks, and outputs a probability for every possible next token.

In each attention layer, every token creates query, key and value vectors. Attention scores, computed from queries and keys, decide how much each token draws information from earlier tokens, so a pronoun can attend to the noun it refers to. Multiple heads capture different relationships in parallel. Stacking dozens of layers builds increasingly abstract representations. Training adjusts billions of weights so predictions match real text, and later fine-tuning teaches the model to follow instructions and hold conversations.

How is the next token predicted in transformers?

The final layer produces a score, or logit, for every token in the vocabulary. A softmax turns these into probabilities, and a decoding method picks one token, which is appended to the input before predicting the next.

Greedy decoding always picks the most likely token, which is predictable but can repeat. Sampling adds variety: temperature reshapes the probabilities, while top-k and top-p limit choices to likely candidates. This loop repeats token by token until the model emits an end token or hits a length limit. A key-value cache stores earlier attention results, so each new token needs only incremental computation. That is why longer outputs take proportionally longer to generate.

What is positional encoding in the transformer model?

Positional encoding gives a transformer information about token order, since self-attention alone treats input as an unordered set. The original transformer added fixed sine and cosine patterns of different frequencies to each token's embedding.

Also asked as: what is the positional encoding in the transformer model

Without position information, the cat chased the dog and the dog chased the cat would look identical. Later models use alternatives: learned position embeddings in GPT-2 and BERT, and rotary position embeddings, called RoPE, in Llama, Qwen and many modern models, which encode relative positions by rotating query and key vectors. ALiBi adds distance-based penalties to attention scores. Methods such as RoPE scaling help extend context length beyond what a model saw during training.

Why is GPT called a transformer?

GPT stands for Generative Pre-trained Transformer: generative because it produces text, pre-trained because it first learns from huge text datasets, and transformer because its architecture is a decoder-only transformer.

OpenAI introduced GPT in 2018, building on the 2017 transformer paper but keeping only the decoder stack with masked self-attention, which lets each token attend only to earlier tokens. This suits next-token prediction. Later versions scaled the same idea with more parameters, more data and training techniques such as reinforcement learning from human feedback. Reasoning models add training that encourages longer thinking before answering, but they remain transformer-based in their core architecture.

When should you use an encoder, a decoder or both in a transformer?

Use encoder-only models, such as BERT, for understanding tasks like classification, search embeddings and entity extraction. Use decoder-only models, such as GPT and Llama, for generating text. Use encoder-decoder models, such as T5, for transforming input into output, like translation or summarisation.

Also asked as: which situation will helpful using encoder or decoder or both in transformer model

Encoders see the whole input at once, which gives rich representations for embeddings and classifiers, and they are smaller and cheaper to run. Decoder-only models dominate today because one model can handle almost any task through prompting, including summarisation and translation. Encoder-decoder models remain strong for speech recognition, as in Whisper, and specialised translation. In RAG systems, an encoder often creates embeddings for retrieval while a decoder writes the answer.

Why do transformers have a fixed input length?

Transformers have a maximum context length because self-attention compares every token with every other, so memory and compute grow quadratically with length, and because models are trained on sequences up to a certain length.

Position encodings are also learned or tuned for a specific range, so performance degrades beyond it. Context limits have grown from 512 tokens in BERT to hundreds of thousands or millions in modern models, thanks to efficient attention kernels such as FlashAttention, RoPE scaling and training on longer sequences. Still, long inputs cost more, and models may overlook information in the middle of long contexts. Exceeding the limit causes truncation or errors, so long documents need chunking.

How do you handle long sequences with transformers?

Split long inputs into chunks that fit the context window, use retrieval to pass only relevant parts, or choose a long-context model. For summarisation, process chunks separately and combine the summaries in a second pass.

Also asked as: how to handle long sequences with transformers

Retrieval-augmented generation is often the best approach: index the document, retrieve the most relevant passages for each question and send only those, which is how RAG.NextUpgrad and the document-scoped RAG in my OCR & Speech Workspace work. For classification of long texts, sliding windows with overlap and aggregated predictions help. Technically, efficient attention variants, such as sparse attention or sliding-window attention, extend practical lengths. Always test whether the model actually uses information from all parts of a long input.

Why is my transformer language model generating meaningless text?

Meaningless output usually comes from a mismatched tokenizer, a wrong chat template, broken or overly aggressive quantization, bad sampling settings such as very high temperature, an undertrained model, or a fine-tune that damaged the base model.

Check that the tokenizer matches the model exactly, and apply the model's chat template for instruction-tuned models. Lower the temperature and set a sensible repetition penalty. If you trained the model, inspect the loss curve: a loss that never fell means undertraining, while gibberish after fine-tuning often signals a learning rate that was too high or data formatting errors. Test the base model first to isolate whether the problem lies in your changes. FineTune Studio's base-versus-tuned comparison catches this early.

What are good transformers workshop ideas?

Good workshop ideas include visualising attention in a small pretrained model, building a tiny character-level transformer from scratch, comparing tokenizers, and fine-tuning a small model on a classification task with Hugging Face Transformers.

For beginners, start with a hands-on demo: tokenize sentences, predict next tokens and adjust temperature to see the effects. For intermediate groups, implement self-attention in NumPy or PyTorch and train a tiny model on a small text dataset, following Andrej Karpathy's nanoGPT. Advanced sessions can cover LoRA fine-tuning, RAG pipelines or evaluation. Keep each session project-based, with notebooks that run on free Google Colab GPUs, so every participant leaves with working code.

Pranjul Rathour

GenAI Engineer · India

GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU) and BYTEBATTLE (MeetKats).

Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. For campus sessions the inviting institution covers the visit end to end: travel (a First AC train ticket for journeys up to five hours, a flight for anything longer), accommodation, meals, and pickup and drop; the honorarium and workshop fees are negotiable on WhatsApp or on the call, and requests are welcome from institutions that can cover the visit this way. Email pranjulrathour41@gmail.com.

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