AI & LLM Implementation
Getting an LLM to produce something impressive in a demo is easy. Getting that capability into production - reliable, cost-controlled, integrated with your stack, and trusted by your team - is a different problem. That's the one we solve.
Model Selection: Choosing the Right LLM
There is no universally best language model. The right choice depends on your use case, latency requirements, cost tolerance, and data sensitivity.
Frontier Models
GPT-4o, Claude Sonnet/Opus, Gemini Pro
Highest reasoning capability. Best for complex analysis, nuanced writing, and multi-step reasoning. 10–20x more expensive than mid-tier. Use when quality justifies the cost premium.
Mid-Tier Models
GPT-4o-mini, Claude Haiku, Gemini Flash
10–20x cheaper with performance sufficient for the majority of production workloads: classification, summarization, FAQ answering. Our default for high-volume tasks until benchmarks say otherwise.
Open-Source Models
Llama 3, Mistral, Qwen, Phi
Run on your own infrastructure. Eliminate per-token API costs at scale, enable fine-tuning on proprietary data, and remove vendor dependency. Tradeoff: operational overhead.
Specialized Models
Embeddings, Vision, Code
Purpose-built models for specific tasks outperform general LLMs: text-embedding-3-large for semantic search, vision models for image understanding, code-specific models for technical tasks.
Core Implementation Patterns
Retrieval-Augmented Generation (RAG)
RAG is the right architecture when your application needs to answer questions grounded in your specific data - product documentation, internal knowledge bases, customer history, legal contracts. Rather than trying to put all your knowledge into an LLM's context window, you store it in a vector database and retrieve only the relevant passages for each query.
A well-built RAG system has several components that all need to work together: document ingestion and chunking, embedding generation, vector storage and indexing, query transformation, retrieval and reranking, and final synthesis. Chunking strategy is consistently underestimated - splitting at arbitrary character counts produces chunks that break mid-sentence, degrading retrieval quality significantly.
Prompt Engineering and Management
Prompt engineering is software development. Prompts deserve version control, testing, staged rollout, and rollback capability. We treat system prompts as configuration artifacts, stored in version control, tested against representative inputs before deployment, and monitored in production for output quality drift.
Structured output - asking the LLM to produce a defined JSON schema rather than free-form text - is one of the highest-impact changes in production AI systems. It makes downstream processing reliable, enables validation, and dramatically simplifies monitoring.
Function Calling & Tool Integration
Modern LLMs can be given a set of function definitions and will intelligently decide when to call them. This is the foundation for agentic systems and also enables powerful simpler integrations - an LLM that can query your product database, check order history, or verify inventory in real time is far more useful than one that can only generate text.
Fine-Tuning
Fine-tuning trains a base model on your specific data. The right approach when you need consistent formatting, have high-volume of a specific task, or need domain-specific knowledge. Not the right first approach - we recommend exhausting prompt engineering and RAG before investing in fine-tuning complexity.
Cost Management
Token costs scale faster than expected. A system costing $50/month in testing can become $5,000/month in production. These are our standard controls.
Model Right-Sizing
Using GPT-4o for every query when GPT-4o-mini handles 80% of them adequately means paying 10–20x more than necessary. We benchmark task-specific performance across model tiers and route to the cheapest model that meets the quality bar.
Context Window Management
Conversational applications accumulate history. We implement intelligent compression: summarizing older turns, retaining only the most relevant context, pruning redundant information. Typically reduces token costs 40–60%.
Semantic Caching
Many LLM queries are semantically identical. Caching embeddings of queries and retrieving cached responses for near-identical inputs reduces API calls 40–60% in customer-facing applications.
Cost Alerting
Hard budget limits with automated alerting when spend exceeds thresholds. The difference between catching an infinite loop at $50 and catching it at $47,000.
Our Implementation Engagement
- Opportunity Mapping
Understand your workflows, identify the highest-value AI use cases, build a business case with realistic cost and quality estimates. 2–3 weeks.
- Proof of Concept
Minimal working implementation of the highest-priority use case. Goal: validate technical feasibility and measure actual performance against your data before committing to full development.
- Production Build
Full integration, monitoring, error handling, testing, and documentation. We build alongside your team so knowledge stays inside your organization.
- Adoption & Support
Training, documentation, feedback loop design, and post-launch monitoring. AI systems require ongoing attention - models update, usage patterns shift, quality can drift.
Related Guides
AI Agentic Experiences →
When you need AI that takes action, not just generates text - autonomous agents, tool use, multi-agent workflows.
LangChain / LangGraph →
The orchestration framework we use for most production AI systems - core concepts, real issues, best practices.
OpenAI API Deep-Dive →
Chat Completions, function calling, embeddings, and the Assistants API deprecation - what you need to know.
FAQ
Strong fit indicators: the task involves natural language understanding or generation; the logic is too complex or variable to encode with traditional rules; accuracy of 85–95% is sufficient; and the cost of errors is manageable. Poor fit: tasks requiring 100% accuracy (financial calculations, legal determinations), tasks where the LLM has no meaningful advantage over a simpler approach, and tasks where data privacy requirements prohibit sending data to external APIs.
This depends on the architecture. A general-purpose assistant using only trained model knowledge needs no proprietary data in the API call. A RAG system sends relevant passages from your knowledge base in each query. A fine-tuned model requires a training dataset. We help clients understand exactly what data flows where and design architectures that meet their data governance requirements.
We establish evaluation frameworks before deployment: curated test sets of representative inputs with expected outputs, automated metrics (accuracy, relevance, hallucination rate), and human evaluation for subjective quality. Monitoring continues in production - tracking output quality against the baseline and alerting when quality drifts.
Yes. Options include open-source models running on your own infrastructure (AWS, Azure, GCP, or on-premises), Azure OpenAI Service (data stays within your Azure tenant), or Anthropic/Google enterprise agreements with data processing terms. We help clients select the deployment model that fits their compliance requirements.
Wrong answers are inevitable. The question is whether your system handles them gracefully. We design for this: confidence scoring that flags uncertain outputs for human review, feedback mechanisms that capture user corrections, escalation paths that route difficult cases to humans, and post-deployment analysis of failure patterns to improve the system iteratively.