Retrieval-Augmented Generation (RAG)
RAG enriches an LLM’s responses by fetching and integrating relevant external knowledge at query time.
Why It Matters
- Cuts AI “hallucinations” by anchoring outputs in factual sources
- Surfaces live, up-to-date information without retraining
Use Cases
- Spire Support Agent
- Internal Knowledge Chatbot
Tech Stack Tags
Azure GPT-4.1 · LangGraph · Pinecone · ChromaLarge Language Models (LLM)
Transformer-based neural networks trained on massive text corpora to generate versatile, human-quality text.
Why It Matters
- Automates summarization, translation, code generation and more
- Drives productivity by handling diverse NLP tasks without custom training
Use Cases
- Spire Support Agent (intent detection & auto-responses)
- Audience Intelligence (targeted campaign generation)
Tech Stack Tags
Azure GPT-4.1 · GPT-4 · Claude · LLaMA · Azure OpenAISmall Language Models (SLM)
Compact transformer models optimized for speed, cost-efficiency, and on-device deployment.
Why It Matters
- Delivers millisecond-level responses for real-time apps
- Reduces infrastructure and energy costs
Use Cases
- Retail FAQ Chatbots
- E-commerce Content Tagging
Tech Stack Tags
Distilled GPT · LoRA-tuned LLaMA · Hugging Face TransformersFine-Tuning
Adapts a pretrained LLM on your proprietary data to learn domain terminology and workflows.
Why It Matters
- Delivers domain-accurate outputs with minimal data
- Cuts development time and cost versus training from scratch
Use Cases
- Insurance Underwriting (30% faster approvals)
- Legal Regulation Extraction
Tech Stack Tags
LoRA · Custom Data PipelinesPrompt Engineering
Crafts structured instructions and examples to steer an LLM toward desired format, tone, and accuracy.
Why It Matters
- Improves output without any retraining
- Ensures consistent, high-quality results
Use Cases
- AI-Powered Test Generation
- Automated Release Notes
Tech Stack Tags
OpenAI API · Prompt TemplatesEmbeddings
Numeric vector representations that map semantically similar data closer together in high-dimensional space.
Why It Matters
- Enables semantic search beyond keyword matching
- Powers personalized recommendations
Use Cases
- Legal Search Engine
- E-commerce Recommendation Engine
Tech Stack Tags
Pinecone · Chroma · OpenAI EmbeddingsAgents / Agentic Workflows
Autonomous AI orchestrations that plan, execute, and self-correct multi-step processes.
Why It Matters
- Automates complex end-to-end tasks
- Adapts dynamically to intermediate results
Use Cases
- Spire Support Agent Lifecycle
- SDLC Copilot (spec → code → tests → PR)
Tech Stack Tags
LangGraph · LangChain · Custom OrchestratorModel Distillation
Compresses a large “teacher” model into a smaller “student” model that retains most capabilities.
Why It Matters
- Speeds inference by up to 3×
- Cuts compute costs in half
Use Cases
- Mobile On-Device Chatbot
- Automated Content Moderation
Tech Stack Tags
Distillation Libraries · TensorFlow LiteVector Database
Specialized stores for indexing and querying vector embeddings instead of text keywords.
Why It Matters
- Sub-millisecond semantic lookups at scale
- Core component for RAG and similar retrieval systems
Use Cases
- Geoscience Natural-Language Queries
- Customer 360 Profile Search
Tech Stack Tags
Pinecone · Chroma · WeaviateGuardrails & Safety
Layers of rules, filters, and monitors that enforce ethical, compliant, and policy-aligned AI outputs.
Why It Matters
- Prevents harmful or biased responses
- Ensures regulatory and privacy compliance
Use Cases
- Financial Chatbot Compliance
- Medical Assistant Safety Checks
Tech Stack Tags
Regex Redaction · Schema FiltersAI Hallucinations
When an LLM confidently fabricates false or misleading information not grounded in data.
Why It Matters
- Undermines trust and reliability
- Carries a high risk in critical domains
Use Cases
- Spire RAG + Schema Validation
- Human-in-the-Loop Escalation Systems
Tech Stack Tags
RAG Layers · Confidence ScoringMulti-modal AI
AI that ingests and reasons over text, images, audio, and video for richer, more context-aware outputs.
Why It Matters
- Delivers a holistic understanding of complex inputs
- Enables novel interactions like video Q&A
Use Cases
- Invoice Image + Text Processing
- Video Ad Voice Cloning
Tech Stack Tags
CLIP · Whisper · Vision-Language TransformersOpen-Source vs Closed-Source Models
Open models (e.g., LLaMA, Falcon) provide public code and weights; closed models (e.g., GPT-4, Claude) are proprietary.
Why It Matters
- Balances flexibility, performance, and cost
- Avoids vendor lock-in with model-agnostic deployment
Use Cases
- Seamless swap between Azure OpenAI & LLaMA
- Enterprise AI Platform best-fit model selection
Tech Stack Tags
ONNX · Triton · OpenAI APIsDomain-Specific LLMs
Models pretrained or fine-tuned on industry data (legal, medical, finance) to master specialized terminology.
Why It Matters
- Delivers higher accuracy on niche tasks
- Ensures out-of-the-box compliance with industry standards
Use Cases
- U.S. Licensing Research Bot
- Financial Invoice Reconciliation
Tech Stack Tags
Custom Fine-Tuning · Domain CorporaRLHF (Reinforcement Learning with Human Feedback)
Aligns model behavior to human preferences by training on a reward model derived from curated feedback.
Why It Matters
- Guides models toward desired tone and ethics
- Enables continuous post-deployment refinement
Use Cases
- Empathy-tuned Support Agent
- Brand-Voice Content Generation
Tech Stack Tags
Reward Modeling · PPO · OpenAI RLHF ToolkitLatency & Inference Optimization
Techniques—quantization, caching, pruning—that minimize AI response times and compute costs.
Why It Matters
- Delivers sub-second replies for real-time systems
- Reduces GPU/CPU usage and overall expenses
Use Cases
- 10 s Ticket Processing (Spire Agent)
- 2× Faster High-Traffic Chatbots
Tech Stack Tags
ONNX Runtime · TensorRT · Quantization Libraries
