Exploring LLM Application Development

Exploring LLM Application Development

What is LLM Application Development?

Large Language Model (LLM) application development involves creating applications that leverage pretrained large language models, like GPT (like GPT3.5, GPT4.o), Sonnet, DALLE, SORA, BERT, T5, Gemma, RoBERTa, DINO, Turning-NLG, Phi, Llama, Stable Diffusion, Flang, Einstine, Megatron, StyleGAN, BART, Granite, or others, to perform natural language processing tasks. Unlike classical applications, which operate on explicit programming logic, LLM-based applications rely on trained models to process human language, make predictions, and respond dynamically based on vast amounts of text data.

LLM app development requires a shift from hardcoded logic to adaptable, learning-based responses. It’s less predictable, involves intensive resources, and focuses heavily on the quality, ethics, and optimization of language-based outputs. In contrast, classical app development relies on deterministic logic, structured data, and typically involves lower complexity in both infrastructure and ethical considerations.

Here’s how LLM app development differs from traditional application development across key aspects:

1. Development Approach

• LLM App Development: Requires fine-tuning pretrained models or adapting prompts for specific tasks rather than writing all logic explicitly. Developers need to understand model parameters, embedding structures, and prompt engineering to optimize the model’s output.
• Classical App Development: Primarily involves traditional programming logic, where algorithms and conditions are hardcoded. Development typically focuses on structured data processing, CRUD operations, or UI/UX functionalities.

2. Data Requirements

• LLM App Development: Data preparation often involves massive unstructured text/image/video/voice data, and developers must address data quality, bias, and labeling. Additionally, data preprocessing, tokenization, and embedding generation are required to ensure the model performs well.
• Classical App Development: Data is often structured, coming from databases or APIs, and requires simpler processing steps. Data is mainly used to drive application behavior, display information, or support basic decision-making.

3. User Interaction and Feedback Loops

• LLM App Development: Applications are designed to respond dynamically to user input, generating language-based outputs in real time. Iterative feedback mechanisms are crucial, allowing the app to improve based on user interactions and performance metrics.
• Classical App Development: Typically follows defined interaction flows with limited scope for variation. Feedback loops focus on usability, functional bugs, or performance improvements, and are less centered around learning from dynamic interactions.

4. Deployment Complexity

• LLM App Development: Deployment is often resource-intensive, requiring powerful hardware (e.g., GPUs) and specialized infrastructure to support the model’s computational demands. It may involve complex configurations like prompt management, API integrations, and multi-modal input handling.
• Classical App Development: Deployment generally involves setting up traditional servers or containers, with a focus on scaling typical compute resources. The architecture is often simpler, with standard scaling configurations.

5. Scalability and Performance Optimization

• LLM App Development: Scalability involves handling high loads of inference requests with low latency, potentially requiring distributed models or specialized hosting solutions. Performance tuning focuses on latency reduction, model compression, and optimizing GPU utilization.
• Classical App Development: Scalability focuses on request throughput and server load balancing. Performance optimization is largely about memory management, efficient algorithms, and reducing response times through optimized queries or code logic.

6. Ethical Considerations

• LLM App Development: Since LLMs generate responses based on training data, ethical concerns are significant. Developers must consider bias, fairness, and transparency, ensuring that outputs align with ethical standards and do not perpetuate harmful stereotypes or misinformation.
• Classical App Development: Ethical considerations are often limited to data privacy, user consent, and security. The application logic is predictable, which reduces the risk of unintended, dynamic responses that could lead to ethical issues.

7. Monitoring and Maintenance

• LLM App Development: Requires ongoing monitoring of model output quality, handling updates as new model versions become available, and incorporating feedback to improve model responses. It often includes retraining and fine-tuning cycles to maintain relevance and quality.
• Classical App Development: Maintenance is more about fixing bugs, updating libraries, or scaling the infrastructure. There’s less focus on iteratively improving application intelligence, as behavior is deterministic and relies on predefined rules.

8. Cost Implications

• LLM App Development: Costs are generally higher due to the need for powerful compute resources, model access fees, storage for large data sets, and potentially high API usage costs. Maintaining an LLM app involves significant infrastructure and operational expenses. We need to pay for every input token, output token, quality of token and speed at which these tokens are generated.
• Classical App Development: Costs are lower, involving standard server infrastructure, database storage, and predictable operational expenses based on user load. Pricing can be optimized through typical cloud scaling without specialized hardware needs.

9. Testing and Evaluation

• LLM App Development: Testing involves more nuanced approaches, such as A/B testing, evaluating model accuracy, relevance of responses, and assessing ethical implications. Test cases focus on both correctness and quality of natural language responses.
• Classical App Development: Testing is usually deterministic, focusing on functional correctness, security, and usability. Automated unit tests, integration tests, and manual QA are sufficient for validation without the need for response quality assessments.

LLM App Development Lifecycle

Keeping LLM app development life cycle in mind we need to take care of many aspects. Broadly they can be grouped as below.

1. Planning and Design

• Objective Definition: 1. Use Case Alignment
• Team Dynamics and Stakeholder Involvement: 31. Cross-Functional Collaboration
• Data Compliance and Ethical Standards: 33. Compliance with Legal and Regulatory Standards
• Customization and Personalization: 29. User-Centric Personalization and Customization
• Model Selection and Optimization: 27. Model Selection and Architecture Optimization
• User Feedback Channels: 11. Feedback Mechanism Design

2. Data Preparation and Model Training

• Data Management and Storage: 6. Data Collection, 7. Data Processing
• Data Quality and Bias Management: 8. Data Quality, 9. Bias and Fairness
• Data Transparency: 32. Model and Data Documentation
• Model Training and Fine-Tuning: 3. Model Fine-Tuning, 4. Model Evaluation, 34. Model Re-Training and Versioning
• Continuous Learning Pipelines: 36. Lifecycle Management and Long-Term Maintenance

3. Development and Prototyping

• Prompt Engineering: 2. Prompt Design
• Dependency Management: 26. Dependency and Infrastructure Management
• Infrastructure Planning: 16. Infrastructure and Compute Management
• Error Handling and Fail-Safes: 30. Robust Error Handling and Fail-Safes
• User Customization Options: 10. Adaptive Responses

4. Testing and Evaluation

• Performance and Latency Testing: 5. Model Performance Optimization, 34. Scalability Testing and Load Management
• Experimentation and A/B Testing: 28. A/B Testing and User Experimentation
• Bias, Fairness, and Explainability: 12. Explainability Mechanisms, 14. Bias and Fairness Evaluation
• Ethical Evaluation: 13. Ethical and Safety Considerations
• User Research: 38. User Research and Post-Deployment Analysis

5. Deployment

• Deployment Infrastructure: 15. Deployment Strategy
• Containerization and Version Management: 17. Model Versioning, 26. Infrastructure as Code (IaC)
• Cost Optimization: 37. Cost Optimization and Budget Management
• Environment Scaling: 25. Multi-Environment Testing, 35. Health Monitoring and Alerts

6. Monitoring and Maintenance

• Real-Time Monitoring and Anomaly Detection: 35. Health Monitoring and Alerts, 27. Scalability Testing and Load Management
• Error Analysis and Debugging: 18. Error Handling and Monitoring
• User Engagement Metrics: 24. Monitoring Usage Patterns, 38. Engagement and Retention Metrics
• Prompt and Model Adaptation: 19. Adaptive Prompt Design
• Feedback Analysis: 11. User Feedback Mechanisms, 34. Iterative Feedback Loop
• Resource Scaling: 16. Infrastructure Scaling and Optimization

7. Post-Deployment Improvement and Iteration

• User Feedback Incorporation: 11. Feedback Mechanisms, 34. User-Centric Feedback Loop
• Bias and Quality Reevaluation: 14. Bias and Fairness Reevaluation, 32. Model Reevaluation
• Re-Training and Updating Models: 34. Model Re-Training and Versioning, 27. Model Maintenance
• Version Rollbacks: 28. Experimentation and Version Rollbacks, 36. Lifecycle Management
• Documentation Updates: 32. Comprehensive Documentation

8. Compliance and Governance

• Regulatory Compliance: 33. Compliance with Legal and Regulatory Standards
• Data Privacy: 6. Data Collection, 33. User Consent and Transparency
• Audit Trails: 14. Bias and Fairness Documentation, 32. Documentation and Audits
• End-of-Life Policies: 36. Lifecycle Management and Long-Term Maintenance

Different Aspects of LLM App Development

To effectively navigate these eight key phases of LLM application development, it is essential to consider the following aspects, as highlighted in the list below.

Building a sustainable and responsible LLM application demands a user-focused approach that ensures resilience. By balancing technical performance with ethical and operational factors, we can provide a high-quality user experience that aligns with industry best practices.

The development of applications utilizing large language models (LLMs) involves a range of considerations across multiple dimensions to ensure the application is performant, reliable, scalable, and secure.

Each of these elements is crucial for creating a strong and successful LLM-powered application. By harmonizing these considerations, we can ensure the application remains user-friendly, dependable, and efficient while adhering to security standards and best practices.

1. Model Selection

• Purpose and Fit: Choose an LLM that aligns with your application’s goals. Consider if the model is optimized for text generation, summarization, translation, code generation, etc.
• Model Size and Latency: Larger models may be more accurate but slower. Assess whether the latency introduced by larger models will affect the user experience.
• Pre-trained vs. Fine-tuned: Decide if the base model’s training is sufficient or if you need to fine-tune it on a specific dataset to improve performance for your use case.
• Provider Options: Consider open-source models like LLaMA, Falcon, or closed ones like GPT-4 or Claude based on licensing and integration needs.

2. Prompt Engineering

• Input Prompt Design: Carefully design prompts to get the most accurate and relevant responses. Include examples or structure the prompts in a way that guides the model.
• Prompt Tuning: For more sophisticated needs, you can tune prompts using techniques like few-shot learning or structured prompt chains.
• Iterative Testing: Test prompts in multiple scenarios to ensure the model responds accurately and consistently across various inputs.

3. Data Privacy and Security

• Data Sensitivity: Avoid sending sensitive or personal data to the model without ensuring secure handling and storage.
• Compliance: Ensure your application complies with relevant privacy regulations (e.g., GDPR, HIPAA) if dealing with sensitive data.
• Token Redaction: Consider anonymizing or redacting sensitive data before sending it to the LLM to mitigate privacy risks.
• Encryption: Use encryption for data in transit (TLS/SSL) and at rest to prevent unauthorized access.

4. Scalability and Infrastructure

• Compute Resources: Allocate adequate resources for hosting LLMs, especially if running a model locally. Consider GPUs and high-memory machines for heavy workloads.
• Autoscaling: If using a cloud provider, set up autoscaling to handle increased traffic and manage costs efficiently.
• Containerization: Use Docker or similar containerization techniques for ease of deployment, portability, and consistency across environments.
• Caching Mechanisms: Cache frequently used responses or use a model endpoint efficiently to reduce redundant processing.

5. Latency and Performance Optimization

• Batch Processing: When applicable, batch requests to improve throughput and decrease latency.
• Token Limits: Keep input lengths within model limits to avoid errors and reduce processing time.
• Distilled or Smaller Models: For low-latency requirements, consider using smaller distilled versions of the model or specialized lightweight models for specific tasks.

6. Monitoring and Logging

• Performance Metrics: Track response times, usage patterns, and model inference times to maintain optimal performance.
• Error Tracking: Log errors and edge cases to identify and resolve issues proactively.
• Content Filtering: Monitor generated content to ensure it aligns with ethical standards, avoids biased outputs, and remains appropriate for the application context.

7. Cost Management

• Usage Patterns: Understand usage patterns to optimize cloud costs, as models, especially large ones, can incur high costs.
• Optimization Techniques: Consider optimizations like prompt engineering, batching, or smaller models to reduce the cost per API call or model run.
• Idle Time Management: Shut down unused resources or instances when not in use, particularly for GPU-backed instances that incur high charges.

8. User Experience and Interaction Design

• Response Quality and Personalization: Tailor responses to feel natural and relevant to the user’s needs. Fine-tuning may be needed for better relevance.
• Controllability: Provide users options to refine responses or rephrase, especially if the model outputs are unclear.
• Failure Handling: Design fallback responses or graceful failure mechanisms for when the model produces unexpected or inaccurate results.

9. Ethics, Bias, and Safety

• Bias Mitigation: Address biases in the model, especially if using it in sensitive applications (e.g., hiring, legal). Test prompts with diverse input scenarios to identify potential biases.
• Content Moderation: Filter or flag inappropriate, harmful, or misleading content that the model might generate.
• Transparency and Explainability: Consider adding an explanation layer to help users understand how and why certain answers were generated, especially in high-stakes applications.

10. Legal and Compliance Aspects

• Terms of Use and Licensing: Be mindful of the model’s licensing terms, especially for commercial applications, and ensure compliance with the provider’s usage restrictions.
• User Consent: Ensure that users are informed about data usage, and where applicable, obtain consent for data processed by the model.
• Audit Trails: Maintain records of interactions and model decisions if required by compliance standards in your industry.

11. Iterative Feedback and Improvement

• User Feedback Mechanism: Collect user feedback on model responses to iteratively improve the application’s effectiveness and relevance.
• Regular Model Updates: Periodically update the model or retrain on new data to keep up with evolving language and domain-specific needs.
• A/B Testing: Experiment with different models or configurations to identify what works best for your application and users.

12. Deployment and Version Control

• Model Versioning: Track different model versions to manage updates or roll back if a new version produces undesired outcomes.
• Staging and Production Environments: Use separate environments for testing and production to avoid disruptions and identify issues before live deployment.
• CI/CD Pipelines: Automate model deployment and testing processes to quickly roll out updates or improvements.

13. Contextual Memory and Session Management

• Session Context: Implement session handling to retain conversational context, especially for multi-turn interactions, which allows the model to provide more relevant responses over time.
• Memory Management: For applications requiring a memory of past interactions, consider implementing a memory module that can persist key user preferences or historical interactions while respecting privacy and data limits.
• Context Window Optimization: Keep track of token limits and intelligently truncate older conversation parts or irrelevant context to optimize responses within the model’s context window.

14. Hybrid Approach with Symbolic and Rule-Based Systems

• Rule-Based Fallbacks: Use rule-based logic as a fallback for certain types of questions or cases where deterministic responses are critical (e.g., legal, compliance-related, or high-stakes queries).
• Symbolic Logic Integration: Combine the LLM with traditional AI methods (like knowledge graphs, ontologies, or expert systems) to enhance reasoning and improve domain-specific accuracy, especially when facts are required.

15. Customization and Fine-Tuning Options

• Domain-Specific Adaptation: Fine-tune the LLM with domain-specific data to improve relevance and performance for specialized applications (e.g., medical, financial).
• Controlled Generation Techniques: Use techniques like Reinforcement Learning from Human Feedback (RLHF) or prompt constraints to steer the model outputs toward desired qualities, especially in specialized use cases.
• User Personalization: Implement user-specific tuning based on personal preferences or past interactions to make the application more personalized and engaging.

16. Explainability and Interpretability

• Model Explainability: Provide explanations of model decisions where feasible, especially in sensitive applications like finance, healthcare, or education.
• Transparency on Data Sources: For applications generating factual information, display sources or cite references where possible to increase trustworthiness.
• Response Justification: Use prompt engineering or additional layers to encourage the model to provide rationale for its outputs when needed, particularly in cases where users may require insight into the model’s logic.

17. Multi-Modal Integration

• Text and Image Processing: If the application involves multi-modal data (like images or audio), integrate LLMs with models capable of processing or analyzing other types of inputs.
• Enhanced User Interaction: Combining LLMs with voice recognition, OCR, or computer vision capabilities can make the application more versatile and interactive (e.g., visual question answering, audio transcription with contextual insights).

18. Dynamic Knowledge Updates

• Real-Time Information: For applications needing real-time information (e.g., news, stock market data), integrate APIs that allow the model to access updated information dynamically.
• Knowledge Retrieval Augmentation: Use external knowledge sources, databases, or retrieval-augmented generation (RAG) techniques to supplement the model’s knowledge base, ensuring it remains accurate and up-to-date without full retraining.

19. Ethical AI and Bias Audits

• Regular Bias Assessments: Periodically audit the model for bias in responses, especially if serving a diverse user base. Address detected biases by adjusting data, prompts, or incorporating fairness-focused fine-tuning.
• Diversity in Training Data: Ensure any additional data used for fine-tuning represents diverse perspectives to mitigate unintended biases.
• Ethics Review Board: Consider setting up an ethics board to review and oversee AI use cases, especially if the application has a high impact on users’ lives or involves sensitive content.

20. User Education and Onboarding

• Guidance on Usage: Educate users on the capabilities and limitations of the LLM-powered features. A well-designed onboarding experience can help set user expectations.
• Transparency about Limitations: Inform users about areas where the model may have limitations or may provide less reliable responses, especially for nuanced or subjective topics.
• Clear Feedback Mechanism: Offer easy-to-use feedback channels so users can report inaccuracies or problematic outputs, which can guide future model improvements.

21. Localization and Multilingual Support

• Multi-Language Capabilities: For global audiences, choose or fine-tune a model capable of handling multiple languages, or integrate language translation APIs if the model supports only a single language.
• Localization for Cultural Relevance: Adapt responses to align with cultural nuances and local contexts, especially for applications like customer support, where cultural relevance can improve user experience.
• Time Zone and Regional Adaptation: Consider regional settings such as time zones, measurement units, or holidays for applications requiring geographical relevance.

22. Security Best Practices for Model and API Usage

• API Throttling and Rate Limiting: Implement rate limiting and throttling to prevent abuse and ensure consistent service for all users, especially when hosting models with high inference costs.
• User Authentication: For sensitive applications, ensure strong authentication and authorization to prevent unauthorized access to the LLM functionalities.
• Data Sanitization: Implement input sanitization to prevent code injection or attacks via crafted inputs, particularly in cases where user input directly interacts with backend systems.

23. Testing and Quality Assurance (QA) for LLM Applications

• Functional and Non-Functional Testing: Perform extensive testing of the model across different types of inputs to ensure both functionality (accuracy, response quality) and non-functional requirements (speed, scalability).
• Edge Case Handling: Identify and handle edge cases in input (e.g., ambiguous, contradictory, or extreme queries) to ensure robust and safe responses.
• Continuous Evaluation: Set up continuous evaluation benchmarks to assess performance over time, tracking any potential drift in quality as usage grows or model updates are deployed.

24. Feedback-Driven Continuous Improvement

• User Feedback Loops: Establish clear feedback loops to gather and analyze user input, which can help improve both prompt design and model fine-tuning.
• Metrics-Driven Adjustments: Regularly review usage metrics, error rates, and other performance indicators to guide iterative improvements and prioritize areas needing attention.
• Human-in-the-Loop (HITL): Consider incorporating human-in-the-loop review processes for applications that require high accuracy, where human moderators review or approve model outputs before they’re shown to users.

25. Sustainable Resource Management

• Eco-Friendly Compute Choices: Opt for energy-efficient hardware and use cloud providers that support sustainable computing practices if the LLM is hosted on cloud infrastructure.
• Resource Throttling During Low Demand: Scale down resources during off-peak hours to reduce environmental impact and operational costs.
• Green AI Strategies: Experiment with model compression, distillation, or quantization techniques to reduce compute needs and make the application more resource-efficient.

26. Dependency and Infrastructure Management

• Dependency Versioning: Track and lock specific versions of model dependencies and libraries (e.g., transformers, tokenizers, and other NLP libraries) to avoid compatibility issues over time.
• Infrastructure as Code (IaC): Use tools like Terraform, CloudFormation, or Ansible to automate infrastructure setup for reproducible deployments, especially in environments that scale or are multi-region.
• Cross-Platform Compatibility: Ensure the infrastructure can support multiple platforms (cloud and on-premises) in case you need to move between providers or maintain hybrid deployments.

27. Model Selection and Architecture Optimization

• Model Size vs. Latency Trade-offs: Depending on your app’s latency requirements, choose a model size that balances performance and response speed (e.g., fine-tuning a smaller model for faster responses if your use case can work with slightly reduced accuracy).
• Ensemble Models: For applications where high accuracy is critical, consider using ensemble methods, combining outputs from multiple models (e.g., LLM with rule-based models or smaller NLP models) to improve robustness.
• Architecture Choice for Scale: If deploying on cloud infrastructure, explore options like Kubernetes, serverless architectures, or API gateways that can dynamically scale with load.

28. A/B Testing and User Experimentation

• Testing New Models and Features: Conduct A/B tests when deploying new models or features to assess performance under real-world conditions and determine user preferences.
• Experimentation Framework: Implement a robust experimentation framework that can track multiple model versions and route users to different models or configurations based on conditions.
• Version Rollbacks: Have a rollback strategy in place if a new model version performs poorly in production, allowing for quick transitions back to more stable configurations.

29. User-Centric Personalization and Customization

• Adaptive Prompting: Adjust prompts based on the user’s history, profile, or interaction patterns to enhance personalization and response accuracy.
• User-Driven Customization: Allow users to set preferences (like response length, detail level, or even “personality” of the AI) that influence the LLM’s behavior, making interactions more tailored and engaging.
• Localization and Accessibility Options: Go beyond language localization to consider accessibility needs, such as voice outputs, screen reader compatibility, and simplified language options for users with different preferences or requirements.

30. Robust Error Handling and Fail-Safes

• Fallback Mechanisms: In cases where the model fails to produce a satisfactory response, implement a fallback to simpler rule-based answers, alternative sources, or even a human support agent.
• Timeout Management: Set response time limits and design the system to handle scenarios where a model response times out, showing a friendly error or retrying the request.
• Error Logging and Analysis: Track and log specific error types, and create custom error messages to improve user experience and gather insights for debugging and future improvements.

31. Cross-Functional Collaboration

• Involve Stakeholders Early: Bring product managers, UX designers, and domain experts into the model development process early on to align the model’s capabilities with user needs and application goals.
• Interdisciplinary Review: Have cross-functional teams (e.g., legal, compliance, customer support) review model outputs and interact with the LLM to identify gaps or potential issues from different perspectives.
• Stakeholder Feedback Loops: Continuously collect feedback from non-technical stakeholders (like customer support) who may observe real-world issues that users face, informing iterative improvements.

32. Model and Data Documentation

• Comprehensive Documentation: Document model architecture, hyperparameters, training data sources, and limitations to support reproducibility, maintenance, and stakeholder understanding.
• Data Source Transparency: Clearly specify where training and fine-tuning data originated, especially if sourced from sensitive or regulated domains, to address legal and ethical considerations.
• Prompt and Response Documentation: For complex applications with numerous prompt templates, keep a record of prompt variations and use cases to help future teams understand prompt choices and effectiveness.

33. Compliance with Legal and Regulatory Standards

• Data Compliance: Ensure data practices align with privacy regulations like GDPR, CCPA, or HIPAA, particularly if personal or sensitive information is used.
• Model Audits and Documentation: For highly regulated industries, maintain audit logs and produce detailed model documentation that can be reviewed by compliance teams or regulators.
• User Consent and Transparency: Inform users when interactions are stored or used for model improvement, and obtain necessary consents, especially when collecting feedback or user data for model updates.

34. Scalability Testing and Load Management

• Load Testing and Stress Testing: Evaluate the system under varying loads to ensure that it can handle peak demand without degradation in response time or accuracy.
• Auto-Scaling and Resource Allocation: For cloud deployments, configure auto-scaling policies to ensure cost-efficient scaling based on real-time load.
• Caching Strategies: Use caching mechanisms for common or repeated queries to reduce model inference costs and improve response time.

35. Health Monitoring and Alerts

• Anomaly Detection: Set up monitoring for abnormal patterns in usage or output quality that could indicate issues like model drift, infrastructure failure, or data issues.
• Service-Level Metrics: Monitor metrics like response latency, error rates, throughput, and system availability to ensure consistent performance.
• Real-Time Alerting: Implement alerting systems to notify engineering teams of critical failures or degradations in model quality or availability.

36. Lifecycle Management and Long-Term Maintenance

• Model Re-Training and Versioning: Develop a schedule for periodic re-training or updating models to improve quality, address new user needs, and handle concept drift.
• End-of-Life Policies: Establish policies for deprecating older model versions and migrating users to newer versions, with clear communication on changes.
• Continuous Learning Pipelines: Set up pipelines to incorporate new data into training workflows, maintaining model relevance without requiring manual intervention.

37. Cost Optimization and Budget Management

• Cost Monitoring: Continuously monitor compute and storage costs, particularly for large models or high-traffic applications, and adjust resources as needed.
• Inference Optimization: Experiment with methods like distillation or quantization to reduce model size and inference costs.
• Flexible Pricing Models: For large user bases, implement flexible usage tiers or subscription models to balance cost and scalability based on user demand and business goals.

38. User Research and Post-Deployment Analysis

• Usability Testing: Conduct user research to understand how the LLM meets user needs and gather insights on improvements or new feature requests.
• Engagement and Retention Metrics: Track user engagement metrics (e.g., time spent, response ratings, return usage) to gauge the application’s effectiveness and user satisfaction.
• Iterative Feedback Loop: Implement a feedback loop to gather post-deployment insights, continuously aligning the model with evolving user needs and expectations.

After looking into all these aspects you would understand how complex is LLM application development. To take care of these we need different set of skills and it is very difficult to find all these skills in one person. If someone have all these skills then that person would be more in decision making, product visioning, solution architecting and that person would not perform any of these task directly, specially on a large size/ high value project.