What techniques can be applied if the retrieved text is too large to fit in the prompt (such as summarization or selecting key sentences), and how do we evaluate the impact of those on answer accuracy?

When retrieved text exceeds prompt limits, two primary techniques are summarization and key sentence selection, each with trade-offs. Summarization reduces text by condensing content, either through extractive methods (selecting existing sentences) or abstractive methods (generating new sentences). Key sentence selection identifies the most relevant portions using algorithms like TF-IDF, BM25, or embedding similarity. Both approaches aim to retain critical information while reducing token usage. A third option is chunking, which splits text into smaller segments processed separately, though this risks losing broader context. The choice depends on the use case: summarization preserves narrative flow, while selection prioritizes precision.

For implementation, developers can use existing libraries or models. For example, extractive summarization might involve a Python library like gensim with TextRank to score sentences by importance. Abstractive summarization could leverage a pre-trained model like BART or T5 via Hugging Face’s transformers. Key sentence selection might use sentence-transformers to embed sentences and the query, then rank them by cosine similarity. Chunking could involve a sliding window with overlap to minimize context loss. Each method requires tuning: adjusting summary length, similarity thresholds, or chunk size. For instance, a QA system might prioritize key sentences matching named entities in the query, while a research tool might prefer summaries to retain connections between ideas.

Evaluating impact on accuracy involves comparing outputs generated with full text versus reduced versions. Metrics include exact match (for factual answers), ROUGE-L (for summary quality), or BERTScore (semantic similarity). Developers can run A/B tests: for example, measure how often a summarization-based answer matches a full-text answer on a test dataset. Practical factors like processing time and cost should also be tracked. If full-text processing is impossible, human evaluation can assess if reduced content still supports correct answers. For example, a medical chatbot using summarization might show 95% accuracy on drug dosage questions but drop to 80% on complex diagnosis queries, indicating where context loss harms performance. Iterative testing helps balance brevity and accuracy.