Text Generation Applications › Summary

Text generation has evolved from a byproduct of language model training into the central interface for a wide range of NLP applications. The key takeaways from this chapter:

• Generation covers a spectrum from open-ended content creation to constrained assistance, each with different quality requirements and different mitigation strategies for the same underlying failure modes
• Quality is multi-dimensional: fluency, relevance, accuracy, coherence, and constraint compliance all matter, with different weights for different applications; no single metric captures all of these simultaneously
• Temperature controls the diversity-coherence trade-off: practical generation tasks operate between 0.7 and 1.2, with lower values for factual precision and higher values for creative variety
• Constraint verification should be automated: programmatic checks catch a significant fraction of output failures before they reach users, and structuring failure types allows targeted retry prompting
• Scoring multiple outputs and selecting the best is more reliable than hoping a single output meets all requirements; the scoring weights encode business priorities and should be calibrated against human judgments
• Prompt structure matters: separating role specification, task description, constraints, and input data into distinct prompt sections reduces violation rates and improves output consistency
• Hallucination is the primary reliability risk: mitigate with retrieval augmentation for factual tasks, and with mandatory human review for high-stakes domains
• Production evaluation requires layered signals: programmatic constraint checking, reference-based metrics, LLM-as-judge, and periodic human evaluation each play different roles in a complete evaluation pipeline

The next chapter, on Summarization, examines a specialized generation regime where the input is a long document and the required output is a concise, faithful reduction of its content. Summarization introduces faithfulness as an additional quality dimension and brings its own evaluation challenges, particularly around the trade-off between compression and information preservation.