Prefill (AI)

Extracted Attributes

• Phase Type

  Initial stage of AI inference

• Security Risk

  Assistant Prefill can be exploited to bypass safety alignments by providing harmful affirmative text

• Primary Function

  Encoding and understanding the input prompt

• Computational Profile

  Computation-intensive and typically GPU-bound or CPU-bound

• Optimization Techniques

  Phase splitting (disaggregation), chunked prefill, and parallel matrix-matrix computations

• Market Leader (Hardware)

  Nvidia (dominant in GPUs for this phase)

Timeline

• Alex Albert of Anthropic highlights the 'Assistant Prefill' feature as a method for users to guide Claude's responses. (Source: Web Search: First Tokens: The Achilles' Heel of LLMs)

  2024-09-01

• Technical educational content is released detailing the distinction between Prefill (understanding) and Decode (generation) stages in AI models. (Source: Web Search: Prefill and Decode in 2 Minutes)

  2025-07-21

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Web Search Results

• Prefill and Decode in 2 Minutes: AI Inference Explained ... - YouTube

  two-step dance. Step one, understanding your prompt completely. Step two, crafting its response one piece at a time. Let's break down what's really happening behind the scenes. Welcome to the prefill stage. This is where magic of understanding begins. During prefill, the AI model reads your entire prompt and converts it into a format it can work with. We call this encoding. Imagine taking a complex idea and translating it into the AI's native language. All of this processed information gets [...] Have you ever wondered what happens inside an AI model when you ask it a question? Today we are diving into this fascinating two-step process that makes AI responses possible. Prefill and decode. Think about how you handle a complex question. You don't just blurt out an answer immediately, right? First you read and understand the entire question. Then you think through your response word by word. AI works remarkably similarly. When you send a prompt to an AI model, it follows this exact [...] # Prefill and Decode in 2 Minutes: AI Inference Explained in Simple Words ## Fahd Mirza 51400 subscribers 40 likes ### Description 1688 views Posted: 21 Jul 2025 Learn how AI language models process your prompts in two distinct stages: Prefill (understanding) and Decode (generating responses token by token). 🔥 Buy Me a Coffee to support the channel: 🔥 Get 50% Discount on any A6000 or A5000 GPU rental, use following link and coupon: Coupon code: FahdMirza

• Prefill Optimization

  Disaggregation of prefill and decoding computations, also known as "phase splitting," is an optimization methods that uses different scheduling and serving of prefill versus decoding computations. Prefill is known to be computation-intensive (i.e. GPU-bound/CPU-bound), whereas decoding is memory-bound and less computation intensive. The different characteristics of these two major phases offers optimizations that separate them, so as to better manage resources in the [...] the (single) activation vector for the next token, the prefill phase can do computations for multiple tokens in parallel. This allows the kernels to be optimized as matrix-matrix computations along the lengthwise dimension of the token sequence, or to use more granular and complex tensor-based computation sequences, such as calculating blockwise portions across multiple GPUs. [...] and some research has been done on this. Speeding up prefill is also important to get a good "latency" or "fast response time" from an AI engine, rather than a delay before the first token is output. 3. Chunked prefill. One type of improved parallelization of prefill is to break the token sequence into "chunks" that are a fixed size. This idea is similar to "batching" and "continous batching" as a general inference optimization,

• Prefill Claude's response for greater output control

  Prefilling is only available for non-extended thinking modes. It's not currently supported with extended thinking. When using Claude, you have the unique ability to guide its responses by prefilling the `Assistant` message. This powerful technique allows you to direct Claude's actions, skip preambles, enforce specific formats like JSON or XML, and even help Claude maintain character consistency in role-play scenarios. [...] The prefill content cannot end with trailing whitespace. A prefill like `"As an AI assistant, I "` (with a space at the end) will result in an error. ### Examples #### Example 1: Controlling output formatting and skipping the preamble [...] had to pick, it would be green because"} # Prefill here {"role": "assistant", "content": "As an AI assistant, I don't have a favorite color, But if I had to pick, it would be green because"} # Prefill here ] ]))

• First Tokens: The Achilles' Heel of LLMs - Invicti

  Assistant Prefill is a relatively little-known feature offered by many LLM providers. I first heard about it in September 2024 from a tweet by Alex Albert (Head of Claude Relations at Anthropic). He was mentioning that when you ask Claude a question you can also provide the first words of the response (you prefill the response). Claude will then start its response as if it already output the text you prefilled. [...] Anthropic even has a whole documentation page related to prefilling Claude's responses. Assistant Prefill is very helpful when you want to control Claude's response, for example if you want to enforce specific formats like JSON or XML. Let's say that you ask Claude a question and you want to receive an answer formatted as JSON. Prefilling the response with `{` will greatly increase the chances that you will receive a JSON response. [...] The article explores the concept of Assistant Prefill, a feature offered by many LLM providers that allows users to prefill the beginning of a model’s response to guide its output. While designed for practical purposes, such as enforcing response formats like JSON or XML, it has a critical vulnerability: it can be exploited to bypass safety alignments. Prefilling a model’s response with harmful or affirmative text significantly increases the likelihood of the model producing unsafe or

• How to Use AI to Prefill Your ATS

  AI technology can lighten this load by joining recruiters in meetings, automatically recording the conversations, transcribing the content, and identifying key points. Rather than relying on recruiters to manually document every detail, AI algorithms can be trained to pull out essential information—such as candidate skills, experiences, and behavioral cues—and organize all candidate data into ATS-ready, structured, searchable data. [...] This can make it difficult for recruitment teams to get the full picture of a candidate, share insights, and ultimately, use the ATS to its full potential. AI steps in to solve this by automatically capturing and consolidating information from multiple sources, like resumes, LinkedIn profiles, and interview transcripts. [...] One of the challenges with manual note-taking is the variation in style and structure between different recruiters, as well as between them and hiring managers. AI can solve this by standardizing the data, organizing notes, skills, qualifications, and experiences in a uniform format across all profiles. Moreover, it can compare information from interviews and other data sources, surfacing inconsistencies in the application process—such as when a candidate's claims vary between sources.