As you work with Retrieval-Augmented Generation (RAG), integrating external knowledge into large language model (LLM) outputs is critical for producing relevant and accurate responses. There are several effective methods for fusing retrieved knowledge with LLM generation. The most common approach is to concatenate retrieved passages or document chunks with the user query, then supply this combined context as input to the LLM. This method, often called context injection, leverages the LLM’s ability to use the provided text when generating its response. Another approach is knowledge distillation, where retrieved facts are summarized or paraphrased before being merged into the prompt, reducing noise and increasing the salience of key information. Some systems use template-based fusion, where retrieved content is slotted into predefined prompt structures to guide the LLM’s focus. More advanced pipelines may use multi-stage integration, feeding retrieved content through intermediate LLM steps (such as summarization or filtering) before final generation.

A central concept in these strategies is the context window—the maximum amount of text an LLM can process at once. This window is measured in tokens, and its size directly limits how much retrieved information can be integrated. If too much content is retrieved, only a subset can be included, or the input must be trimmed or compressed. This limitation is especially important in RAG, since including too much irrelevant or redundant information can dilute the model’s focus, while including too little may omit key facts. Effective knowledge integration, therefore, balances completeness with conciseness, ensuring that only the most relevant pieces of evidence are presented to the LLM within its context window.