Context Engineering for SaaS Applications

Software-as-a-Service product powered by AI as a core capability, not just an add-on feature.

SaaS product designed from the ground up with AI as the primary value driver, not retrofitted with AI features.

Large Language Model: AI model trained on vast text data to generate human-like text, reason, and follow instructions.

Small Language Model: compact AI model optimized for specific tasks with lower cost and latency than LLMs.

Retrieval-Augmented Generation: technique combining information retrieval with LLM generation to ground responses in specific data.

Architecture where a single software instance serves multiple tenants (customers) with data isolation and resource sharing.

Unit of text processed by an LLM. Token costs are the primary variable cost in AI SaaS.

Intelligent selection of AI models based on task complexity, cost, latency, and quality requirements.

Centralized service that routes AI requests, manages costs, provides fallbacks, and enforces policies across multiple AI providers.

Database optimized for storing and searching vector embeddings, enabling semantic search and RAG.

Numerical vector representation of text or data that captures semantic meaning for similarity search.

AI system that can plan, use tools, execute actions, and iterate toward a goal with varying degrees of autonomy.

Model Context Protocol: standard for connecting AI models to external tools and data sources.

AI model capability to invoke external functions/APIs based on user intent and context.

Security attack where malicious instructions are embedded in data to manipulate AI model behavior.

Architectural guarantee that one tenant cannot access another tenant data or affect their AI performance.

Cost of Goods Sold for AI services, including inference costs, API costs, and infrastructure costs.

Net Revenue Retention: measures revenue growth from existing customers including expansion, contraction, and churn.

Product-Led Growth: go-to-market strategy where the product itself drives acquisition, activation, and expansion.

Operational practices for deploying, monitoring, and managing LLM-based applications in production.

Systematic assessment of AI model quality, accuracy, safety, and cost across defined metrics and test cases.

Maximum number of tokens an LLM can process in a single request, influencing cost and capability.

Process of training a pre-trained model on domain-specific data to improve performance for specific tasks.

Technique for delivering AI responses incrementally as they are generated, reducing perceived latency.

Cache that stores AI responses and retrieves them for semantically similar queries, reducing redundant inference costs.

Performance issue where one tenant heavy AI usage degrades performance for other tenants in shared infrastructure.

Pricing model where customers pay based on successful AI outcomes rather than usage or seats.

Pricing model where customers pay based on actual AI consumption (tokens, requests, transactions).

Collection of AI agents that collectively perform business processes with varying levels of autonomy.

AI assistant that works alongside humans, suggesting actions but requiring human approval for execution.

AI system that can execute tasks independently within defined policy boundaries without human approval.

AI workflow pattern where human approval is required for certain actions, balancing automation with oversight.

Centralized repository for managing, serving, and monitoring ML features used in AI applications.

Practice of managing prompts as versioned artifacts with change tracking, testing, and rollback capabilities.

Framework of policies, processes, and controls for ensuring AI systems are safe, fair, accountable, and compliant.