Best Practices for Designing and Developing an AI Solution

These days lots of AI tools and options are available for building solutions.  Every day there are optimizations and newer improvements coming into the AI tools that are available on the market.

Initially, we started with predictive models and are now learning to use generative models and tools such as GPT, OpenAI, Claude, Copilot, Gemini, Ollama, Mistral, Transcribe, and MS Power Automate, with the list continuing to grow. Various cloud providers have introduced multiple models and agentic workflow support options, while modern data platforms, development frameworks, and operational tools are increasingly being enhanced with AI capabilities.

The choices are many, and all we need to do is select the tool or service that best fits our solution requirements.
In today’s software development landscape, AI tools can help us identify issues in code, generate functionality from simple prompts, and accelerate development efforts with minimal context. As new capabilities continue to emerge, we are constantly exploring better and more efficient ways to build solutions.

This rapid advancement has transformed how organizations approach software development. As developers, managers, architects, and leaders, we are all benefiting from faster workflows, improved productivity, and greater automation, helping organizations move steadily towards becoming AI-driven.

While these advancements have created significant opportunities an effective AI solution requires careful consideration of several key factors.

What Often Gets Overlooked in AI Solution Development

Amidst the excitement of rapid AI adoption, there are several critical considerations that are often overlooked during solution design and implementation.

Security 

Besides the user and request identity access management, AI security has an additional layer of security to be incorporated 

• OAuth 2.0/2.1, RBAC-combined with User behavior analytics (UEBA) to detect threats
• Automated vulnerability scans, Code reviews for low code and generated code and workflow approvals
• Clauses in AI service agreement like Data privacy and security, Data ownership and responsibility, Confidentiality, Law compliance
• Encryption of data at rest and in transit
• Measures to avoid data poisoning that tampers training data to corrupt model performance
• Measures to address adversarial attacks – crafting inputs so that AI makes mistakes    
• Implement zero-trust architecture

Security is one of the major areas to be addressed throughout the AI implementation cycle. The above are a few but it is not limited to these measures alone. By incorporating security, the organization is saved from huge losses arising from losing client’s trust to legal issues arising from data violations.

Validation and Guardrails 

• Protocol – MCP specific protocol rules 
• Input – Sanitize data inputs to prevent model poisoning 
• Output - Block/sanitize the content, structural validation, Context-aware encoding, Human-in-the-loop for approval, Guardrails AI, Web Sanitization tools

Testing and Responsible AI

• HITL
• Adversarial Testing
• Advanced testing for non-deterministic systems
• Bias and fairness Monitoring
• Explainability checks to ensure AI be transparent and understandable, especially in applications like finance and healthcare
• Trust Risk and Security Management

Scalability and High Availability

For organizations adopting Model Context Protocol (MCP)-based architectures, scalability and availability become important design considerations.

• Deploying multiple redundant MCP servers across different availability zones ensures high availability and allows for horizontal scaling based on demand
• Containerization and Orchestration
• Load Balancing and Gateways
• Distributed State Management
• Microservices architecture
• Optimize Payloads and token usage
• Caching
• Asynchronous I/O
• Connection Pooling
• Health checks, Failover
• Continuous Monitoring and Observability, Performance measurement

Accuracy and Hallucination Control

• Multi-layered approach combining high-quality training data, curate high quality data and regular updates
• Retrieval-augmented generation (RAG)
• Strict prompting
• Clearly defining constraints and instructing AI to restrict inventing answers
• Utilize citations to fact check and claims sources
• Identify the models to check which factors influence the hallucination rates 

Load Balancing and Session Management

• HAProxy: A widely used open-source load balancer that supports mcp-session-id header-based stickiness and protocol validation. 
• Nginx, Envoy/Istio
• Cloud Providers: AWS Network Load Balancer (NLB), AWS Application Load Balancer (ALB), GCP Global load balancing, Azure Front door and APIM

Cost Optimization

• Rate limiting
• Caching
• Model Selection
• Monitor usage and Forecast budge breaches
• Optimize prompts to reduce token output
• RAG usage over fine tuning and expensive full training

GPU and Infrastructure Optimization

• Maximizing GPU performance for graphics and AI involves optimizing memory bandwidth, utilizing parallel processing cores, and minimizing idle time. 
• Employing mixed-precision training
• Profile and monitor GPU usage and workloads
• Optimizing data pipelines
• Implementing proper memory management to prevent out-of-memory errors
• Distributed training for large workloads

Compliance and Governance

• Establish AI Governance\
• Define Legal, ethical, unbiased, non-discriminatory and privacy protected regulations 
• AI Standards long with other regulations like GDPR, HIPAA
• Data Governance & Privacy
• Model Explainability & Transparency
• Enforce regular Bias Audits
• Usage of approved Tools defined by the organization governance
• Establish an internal registry for all approved MCP servers
• Monitoring – SIEM, NDR
• Define Operations policies

To sum it up, these are some of the points to be kept in mind while designing and building an AI application.

Building an AI solution is no longer just about selecting the right model or framework. Success depends on balancing innovation with security, scalability, governance, performance, and cost efficiency throughout the solution lifecycle.

While every organization is racing to call themselves AI-driven, following these measures and best practices in AI, is what makes it a truly AI-driven organization.

This blog is part of ThoughtForce, an initiative by Xoriant to showcase insights from its House of XFactors, driving thought leadership through collective expertise.