Principle:Deepset ai Haystack LLM Text Generation

Overview

LLM text generation uses large language models to produce natural language responses given a prompt, serving as the generative backbone of RAG and conversational systems. It is the core generation step that transforms a structured prompt into coherent, contextually relevant text output.

Domains

• NLP
• Generation

Theory

LLM text generation is based on autoregressive language modeling, where the model generates tokens sequentially, each conditioned on all previous tokens. The generation process employs various decoding strategies to control the quality, diversity, and determinism of the output.

Autoregressive Generation

In autoregressive generation, a language model computes a probability distribution over the vocabulary for the next token, given the sequence of preceding tokens (both the input prompt and previously generated tokens). Formally, for a sequence of tokens x_1, x_2, ..., x_n, the model estimates:

P(x_{n+1} | x_1, x_2, ..., x_n)

Generation proceeds token by token until a stopping condition is met (e.g., an end-of-sequence token, a maximum token count, or a stop sequence).

Decoding Strategies

Several strategies are used to sample from the predicted token distribution:

• Temperature sampling: Scales the logits before applying softmax. A temperature of 0 (greedy/argmax sampling) always picks the most likely token, producing deterministic output. Higher temperatures (e.g., 0.9) flatten the distribution, increasing randomness and creativity.

• Nucleus sampling (top_p): Restricts sampling to the smallest set of tokens whose cumulative probability exceeds a threshold p. For example, top_p=0.1 considers only tokens comprising the top 10% of probability mass. This adaptively narrows the candidate set based on the distribution shape.

• Top-k sampling: Restricts sampling to the k most likely tokens. This provides a fixed-size candidate set regardless of the probability distribution.

• Greedy decoding: Always selects the highest-probability token. Fast and deterministic, but can produce repetitive or low-quality text.

Generation Parameters

Key parameters that control text generation behavior:

• max_completion_tokens: Upper bound on the number of generated tokens, including both visible output tokens and any internal reasoning tokens.
• stop sequences: One or more token sequences that signal the model to stop generating.
• presence_penalty: Penalizes tokens that have appeared at all in the text, discouraging repetition of topics.
• frequency_penalty: Penalizes tokens proportional to how frequently they have appeared, discouraging verbatim repetition.
• logit_bias: Directly adjusts the log-probability of specific tokens, allowing fine-grained control over token selection.
• n: Number of independent completions to generate for a single prompt, useful for selecting the best response.

Streaming

Modern LLM APIs support streaming responses, where tokens are delivered incrementally as they are generated rather than waiting for the full completion. This reduces perceived latency and enables real-time display of generated text. Streaming is implemented through callback mechanisms that process each chunk as it arrives.

Role in RAG Systems

In Retrieval-Augmented Generation (RAG) pipelines, text generation is the final step where the LLM synthesizes an answer based on:

1. The user's original query
2. Retrieved documents or passages
3. Any system-level instructions

The prompt template (constructed upstream) provides the LLM with all necessary context, and the generator produces the final natural language response.

Related Pages

• Implementation:Deepset_ai_Haystack_OpenAIGenerator