mlsanityData to 14 Oct 2026

Beyond the Comfort Zone: Emerging Solutions to Overcome Challenges in Integrating LLMs into Software Products

arXiv 2410.12071 ↗First posted 15 Oct 2024cs.SE cs.LGcited by 11

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Abstract (arXiv)

Large Language Models (LLMs) are increasingly embedded into software products across diverse industries, enhancing user experiences, but at the same time introducing numerous challenges for developers. Unique characteristics of LLMs force developers, who are accustomed to traditional software development and evaluation, out of their comfort zones as the LLM components shatter standard assumptions about software systems. This study explores the emerging solutions that software developers are adopting to navigate the encountered challenges. Leveraging a mixed-method research, including 26 interviews and a survey with 332 responses, the study identifies 19 emerging solutions regarding quality assurance that practitioners across several product teams at Microsoft are exploring. The findings provide valuable insights that can guide the development and evaluation of LLM-based products more broadly in the face of these challenges.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 31 of its 71 references that are in the map. First posted on arXiv 15 Oct 2024.

Builds on

31 of 71 references.

References · 31

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  3. 2023Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formattingcited by 817
  4. 2023Automatic Prompt Optimization with "Gradient Descent" and Beam Searchcited by 653
  5. 2024A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applicationscited by 431
  6. 2023NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Railscited by 365
  7. 2020AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Promptscited by 249
  8. 2021Design Guidelines for Prompt Engineering Text-to-Image Generative Modelscited by 218
  9. 2024Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferencescited by 144
  10. 2022PromptChainer: Chaining Large Language Model Prompts through Visual Programmingcited by 134
  11. 2023EvalLM: Interactive Evaluation of Large Language Model Prompts on User-Defined Criteriacited by 74
  12. 2023Unleashing the potential of prompt engineering for large language modelscited by 72
19 more
  1. 2022Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Modelscited by 70
  2. 2024Evaluating LLMs at Detecting Errors in LLM Responsescited by 50
  3. 2024A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Taskscited by 47
  4. 2020Where Responsible AI meets Reality: Practitioner Perspectives on Enablers for shifting Organizational Practicescited by 39
  5. 2024Evaluating the Evaluator: Measuring LLMs' Adherence to Task Evaluation Instructionscited by 34
  6. 2023ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into Principlescited by 33
  7. 2024Farsight: Fostering Responsible AI Awareness During AI Application Prototypingcited by 28
  8. 2024Artificial Intelligence Index Report 2024cited by 24
  9. 2023(Why) Is My Prompt Getting Worse? Rethinking Regression Testing for Evolving LLM APIscited by 23
  10. 2023A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitionerscited by 22
  11. 2023ScatterShot: Interactive In-context Example Curation for Text Transformationcited by 20
  12. 2023PromptAid: Prompt Exploration, Perturbation, Testing and Iteration using Visual Analytics for Large Language Modelscited by 19
  13. 2024SPADE: Synthesizing Data Quality Assertions for Large Language Model Pipelinescited by 19
  14. 2024Prompts Are Programs Too! Understanding How Developers Build Software Containing Promptscited by 19
  15. 2023Building Your Own Product Copilot: Challenges, Opportunities, and Needscited by 17
  16. 2024Rethinking Software Engineering in the Foundation Model Era: From Task-Driven AI Copilots to Goal-Driven AI Pair Programmerscited by 12
  17. 2024Development in times of hype: How freelancers explore Generative AI?cited by 8
  18. 2023Visualizing Linguistic Diversity of Text Datasets Synthesized by Large Language Modelscited by 4
  19. 2024RETAIN: Interactive Tool for Regression Testing Guided LLM Migrationcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 7.

11 papers.

  1. 2025A Large-Scale Evolvable Dataset for Model Context Protocol Ecosystem and Security Analysiscited by 7
  2. 2025LLM Company Policies and Policy Implications in Software Organizationscited by 2
  3. 2026Agentic Pipelines in Embedded Software Engineering: Emerging Practices and Challengescited by 2
  4. 2026PrefPO: Pairwise Preference Prompt Optimizationcited by 2
  5. 2026How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Studycited by 2
  6. 2025A Research Roadmap for Augmenting Software Engineering Processes and Software Products with Generative AIcited by 1
  7. 2026Key Considerations for Domain Expert Involvement in LLM Design and Evaluation: An Ethnographic Studycited by 1
  8. 2025The Hitchhikers Guide to Production-ready Trustworthy Foundation Model powered Software (FMware)cited by 0
  9. 2026Results-Actionability Gap: Understanding How Practitioners Evaluate LLM Products in the Wildcited by 0
  10. 2026Beyond the Org Chart: AI and the Transformation of Invisible Workcited by 0
  11. 2026A Framework for Generating Valid Context-Specific Benchmarks through Expert Guidancecited by 0