Additional Resources
Technical FAQ & Troubleshooting
This section addresses the most common technical, implementation, and product-related questions we receive from clients and partners. It’s designed to support engineering teams, technical buyers, and power users working closely with DigitalBoutique.ai systems.
AI Systems & Custom Solutions
How do you ensure custom AI systems are maintainable over time?
We design all systems with a modular, documentation-first architecture. Each component is versioned, API-driven, and independently swappable — reducing technical debt and making long-term maintenance seamless.
What happens if an LLM starts hallucinating?
We integrate hallucination detection tooling (e.g., RAGAS, automated data audits), monitor model drift, and apply ground-truth validation pipelines. When necessary, we use prompt tuning or retraining informed by QA results.
Can I host the system in my own infrastructure?
Yes. We support client-hosted deployments via VPC or private cloud environments. Secure deployment requires IAM onboarding and access provisioning. Please note: self-hosting incurs additional deployment fees compared to fully-managed options.
Can you build systems that work across multiple languages?
Absolutely. We support multilingual models and localization layers tailored to global use cases such as international customer support and multi-language knowledge bases.
Do you support RAG (Retrieval-Augmented Generation)?
Yes — this is one of our core specialties. We build scalable RAG pipelines with vector search, semantic filters, and grounded response generation. Our QA process measures precision, recall, and groundedness to ensure accuracy.
Infrastructure & DevOps
How do you handle deployment and updates?
We configure GitHub workflows or n8n-based CI/CD pipelines for deployment. Staging and production environments are isolated, with regression testing for every major update before release.
What infrastructure do you use?
Our environments are provisioned using Docker, Terraform, Railway, or Kubernetes. For security, we implement environment-level encryption, secrets management, and strict least-privilege role assignments.
Troubleshooting & Edge Cases
What if I get inconsistent responses?
Submit logs and example prompts to our support team. We use automated retries, fallback models, and circuit-breaker patterns to reduce inconsistency.
How do you handle long response times?
We profile latency using request tracing and async execution logs. Optimizations may include caching, prompt trimming, or alternative model selection for faster turnaround.
Glossary: Key Terminology
- LLM (Large Language Model): A neural network trained on massive text corpora, capable of generating human-like text.
- RAG (Retrieval-Augmented Generation): A technique that combines LLMs with external knowledge sources (e.g., vector databases) to improve factual accuracy.
- Vector Database: A specialized storage engine for embeddings (numeric text representations) used in semantic search and retrieval.
- Embedding: A numerical vector that represents the meaning of text or other data.
- Prompt Engineering: Crafting and refining prompts to elicit specific outputs from AI systems.
- Groundedness: A measure of how well an AI output aligns with source documents or trusted data.
- Token Limit: The maximum number of tokens (words/characters) a model can process in one request.
- Agent: An AI system or service that performs tasks autonomously or semi-autonomously (e.g., research, classification, conversation).
- Human-in-the-Loop (HITL): A workflow where humans review, approve, or override AI outputs.
- CI/CD (Continuous Integration/Continuous Deployment): Automated pipelines that test, validate, and release software safely to staging and production environments.
This resource is continuously updated as our technology evolves. If you don’t see your question here, please reach out to our support team — we’ll help directly and expand this guide for future users.