artificial intelligence software requirements

This is a broad topic, as "Artificial Intelligence Software" can range from a simple chatbot to a complex autonomous driving system. The requirements vary drastically based on the type of AI (e.g., Machine Learning, Natural Language Processing, Computer Vision, Generative AI). Below is a comprehensive breakdown of requirements categorized by functional (what it does) and non-functional (how it performs). This serves as a template you can adapt for your specific AI project. I. Functional Requirements (The "What") These define the specific behaviors and capabilities the AI software must have. Core AI/ML Functionality FR-01: Data Ingestion: The system shall be able to import data from various sources (CSV, JSON, SQL databases, APIs, real-time streams). FR-02: Data Preprocessing: The system shall provide capabilities to clean, normalize, transform, and augment data (e.g., handling missing values, tokenizing text, resizing images). FR-03: Model Training: The system shall support training specific model architectures (e.g., CNN for images, LSTM/Transformer for text) using specified algorithms. FR-04: Model Inference: The system shall accept input (e.g., image, text, sensor data) and return a prediction, classification, or generated output. FR-05: Model Retraining: The system shall allow for retraining on new data without full system downtime. FR-06: Explainability (XAI): The system shall provide human-readable explanations for its decisions (e.g., heatmaps, feature importance scores). User Interaction & Interface FR-07: Input Interface: The system must accept user input via expected channels (e.g., Web UI form, REST API, voice command). FR-08: Output Presentation: The system shall display results in a clear format (e.g., dashboard, JSON response, synthesized speech, image with bounding boxes). FR-09: Feedback Loop: Users should be able to correct or rate the AI's output (e.g., "This answer was wrong"). This data should be logged for improvement. Management & Admin FR-10: Model Versioning: The system must track different versions of trained models with metadata (date, accuracy, data used). FR-11: Experiment Tracking: The system shall log all training runs including hyperparameters, metrics, and code versions. FR-12: User Management: The system shall authenticate users and enforce role-based access control (e.g., Admin, Developer, End-User). II. Non-Functional Requirements (The "How") These define the quality attributes of the system. Performance & Scalability NFR-01: Latency: For a standard query, inference time must be < 500ms (critical for real-time apps like chatbots or fraud detection). Batch processing can be slower. NFR-02: Throughput: The system must handle X requests per second (TPS) at peak load (e.g., 1000 TPS). NFR-03: Scalability: The system must be horizontally scalable to handle increasing data volume and user load (often via Kubernetes or serverless functions). NFR-04: Resource Consumption: Model size should be < 1GB for edge deployment; cloud deployment allows larger models but must meet cost constraints. Reliability & Availability NFR-05: Availability: The core inference service shall have an uptime of 99.9% (excluding planned maintenance). NFR-06: Fault Tolerance: If a GPU fails or a model crashes, the system should auto-failover to a backup instance without data loss. NFR-07: Data Durability: Training data and models must be backed up and recoverable (e.g., stored in replicated cloud storage like S3). Security & Privacy NFR-08: Data Privacy: The system must comply with GDPR / CCPA. User data (input and output) must be encrypted at rest (AES-256) and in transit (TLS 1.3). NFR-09: Adversarial Robustness: The system should be tested against common adversarial attacks (e.g., input manipulation, poisoning) and logged if detected. NFR-10: Audit Trail: All access to sensitive data and model changes must be logged with a timestamp and user ID. NFR-11: Access Control: API keys or OAuth 2.0 tokens must be required for all inference calls. Maintainability & Deployability NFR-12: Modularity: The system architecture should separate concerns: Data Layer, Training Pipeline, Inference Service, and UI. NFR-13: CI/CD: The system must support automated testing and deployment pipelines for model updates (MLOps). NFR-14: Monitoring: The system shall expose metrics (CPU/GPU usage, memory, request latency, model drift) via a logging system (e.g., Prometheus, Grafana). Ethics & Bias NFR-15: Fairness: The system shall be evaluated for bias across demographic groups (race, gender, age) and provide a fairness report. NFR-16: Safety: For high-stakes domains (medical, autonomous driving), the system must have a "human-in-the-loop" override mechanism. III. Infrastructure & Technology Stack Requirements This is a "how to build it" checklist. Component Example Technologies : : Compute (Training) GPU (NVIDIA A100, H100), TPU (Google), AWS SageMaker, Azure ML Compute (Inference) NVIDIA Triton Inference Server, ONNX Runtime, TorchServe, TensorFlow Serving Data Storage AWS S3, Azure Blob, Google Cloud Storage, Snowflake, BigQuery Orchestration Kubernetes (K8s), Docker, Apache Airflow, Kubeflow Model Registry MLflow, DVC (Data Version Control), Weights & Biases Monitoring Prometheus, Grafana, ELK Stack (Elasticsearch, Logstash, Kibana) APIs FastAPI (Python), Flask, gRPC, GraphQL IV. Example Use-Case Specific Requirements A. Chatbot (LLM-based) Functional: Must maintain conversational context for >5 turns. Must refuse harmful prompts. Non-Functional: Time-to-first-token < 2 seconds. Hallucination rate < 5%. B. Computer Vision (Quality Inspection) Functional: Detect 12 types of defects (syntax: bounding boxes + confidence score). Output image with overlay. Non-Functional: False negative rate < 0.1%. Frame rate > 30 FPS for video. C. Recommendation System Functional: Generate personalized top-10 list for each user based on history + real-time behavior. Non-Functional: Model retraining frequency = daily. Cold-start latency for new users < 1 hour. V. Important Special Considerations for AI Data is a Requirement: You cannot write AI requirements without understanding the training data. Specify data quality, volume, labeling precision, and data drift monitoring. ML Ops vs. Traditional Ops: Standard software "bugs" are deterministic. AI has "performance decay". Requirements must include model drift detection and automated retriggers. The "Black Box" Problem: For regulated industries (finance, healthcare), the requirement for explainability (SHAP, LIME) is often a legal, not just technical, requirement. Final SRS Template Structure (for your document) Introduction (Purpose, Scope, Definitions) Overall Description (Product perspective, User characteristics, Assumptions) Specific Requirements - 3.1 Functional Requirements (as listed above, numbered) - 3.2 Non-Functional Requirements (as listed above, numbered) - 3.3 External Interface Requirements (UI, API, Hardware) - 3.4 Data Requirements (Volume, format, labeling schema) - 3.5 Ethical & Legal Compliance Appendices (Glossary, Model Architecture Diagrams, Data Dictionary) Key takeaway: Start by defining one clear problem the AI solves. Then choose the requirements from the list above that directly support that solution. Avoid "feature creep" (adding every possible AI capability).