When retrieved text exceeds prompt limits, three practical techniques are summarization, key sentence selection, and chunking with prioritization. Summarization reduces content by distilling main ideas, either through extractive methods (selecting existing sentences) or abstractive methods (generating new sentences). Key sentence selection identifies critical sentences using metrics like similarity to the query (e.g., cosine similarity with embeddings) or salience scores (e.g., TF-IDF). Chunking splits text into smaller segments (e.g., paragraphs or fixed token windows) and prioritizes the most relevant ones. For example, a question-answering system might filter chunks by overlap with keywords or embed them to rank relevance before feeding top segments to the LLM.
To evaluate impact on accuracy, use controlled experiments comparing outputs from full-text versus processed inputs. For summarization, measure if key facts are preserved by checking answer correctness against a ground-truth dataset. For key sentence selection, track how often omitted sentences affect answers (e.g., via ablation studies). Chunking can be tested by varying chunk sizes and overlap thresholds to find a balance between coverage and token limits. Automated metrics like ROUGE (for summarization) or BLEU (for answer similarity) provide quantitative benchmarks, but human evaluation is critical for nuanced tasks. For instance, if a summarization step drops a critical detail needed to answer "What caused Event X?", accuracy drops even if the summary is coherent.
Implementation trade-offs depend on the task. Summarization risks losing context, while chunking may fragment information. For example, in legal document analysis, summarization might omit case-specific nuances, but chunking with overlap (e.g., sliding windows) preserves local context. Always validate with domain-specific tests: if processing steps reduce accuracy by over 10% in a QA task, consider hybrid approaches (e.g., combining summarization and key sentence selection) or iterative refinement. Tools like LlamaIndex or LangChain offer built-in evaluation methods, such as response relevance scoring, to automate impact assessment. Ultimately, the choice depends on balancing token constraints with minimal information loss for the target use case.