Glossary: AI & Document Processing Terms
For business decision-makers: This glossary translates technical AI terminology into plain English, explaining what terms mean for your business.
For technical teams: Quick reference for explaining concepts to non-technical stakeholders.
Core AI Concepts
Context Window
Technical Definition: The maximum amount of text (measured in tokens) that a large language model can process in a single interaction.
Business Translation: How much information an AI can “read” at once. Think of it as the AI’s working memory. Most models can handle 50-200 pages at a time, but enterprise documents often exceed this limit.
Why It Matters: Larger context windows cost more to use. A 1M token context window might cost 10-100x more per query than a 10K window. Smart retrieval strategies avoid this cost while delivering better results.
Related Terms: Token, Token Consumption, Context Length
Token
Technical Definition: The basic unit of text processing in language models. A token is roughly 0.75 words in English.
Business Translation: Processing units that determine AI costs. 1,000 tokens ≈ 750 words ≈ 1-2 pages of text.
Why It Matters: AI services charge per token processed. A 5,000-page document contains ~2-3 million tokens. Processing this repeatedly with standard approaches becomes expensive quickly.
Typical Sizes:
- Email: 500-1,000 tokens
- 10-page report: 5,000-7,500 tokens
- 100-page contract: 50,000-75,000 tokens
- 5,000-page due diligence set: 2.5-3.75 million tokens
Related Terms: Context Window, Token Consumption
Embedding / Semantic Embedding
Technical Definition: A numerical vector representation of text that captures semantic meaning, enabling similarity comparisons.
Business Translation: Converting text into numbers that preserve meaning. Similar concepts get similar numbers, allowing AI to find related information even when exact words differ.
Why It Matters: Enables semantic search—finding documents about “contract termination” even when they use words like “agreement cancellation” or “deal dissolution.”
Example:
- “automobile” and “car” have similar embeddings
- “bank” (financial) and “bank” (river) have different embeddings based on context
Related Terms: Vector Database, Semantic Search
Vector Database
Technical Definition: A specialized database optimized for storing and searching high-dimensional vector embeddings.
Business Translation: A search engine that finds similar meanings, not just matching keywords. It stores the numerical representations of your documents and finds the most relevant ones for any question.
Why It Matters: Powers fast semantic search across millions of documents. Answers like “find contracts with termination clauses” even when documents use varying terminology.
Common Examples: ChromaDB, Milvus, Elasticsearch, Pinecone
Related Terms: Embedding, RAG, Semantic Search
Large Language Model (LLM)
Technical Definition: A neural network trained on vast text corpora to understand and generate human-like text.
Business Translation: The AI that reads text and generates answers. Examples include GPT-4 (OpenAI), Claude (Anthropic), and Gemini (Google).
Why It Matters: The quality of answers depends on both the LLM and how you provide information to it. A great LLM with poor context management delivers poor results.
Related Terms: GPT-4, Claude, Gemini, Context Window
Document Processing Techniques
RAG (Retrieval-Augmented Generation)
Technical Definition: A technique that retrieves relevant document chunks and provides them as context to an LLM, rather than loading entire documents.
Business Translation: Smart search across massive documents. Instead of forcing the AI to read everything (expensive and slow), RAG finds only the relevant sections and sends those to the AI.
Why It Matters:
- Works with documents of any size
- 70% lower processing costs than full-document approaches
- Sub-second response times
- Exact citations for audit trails
Best For: Specification searches, contract Q&A, compliance checks, policy lookups
Related Terms: Chunking, Vector Database, Semantic Search
GraphRAG
Technical Definition: An extension of RAG that builds a knowledge graph of entities and relationships, using graph traversal to retrieve context.
Business Translation: Understanding connections between documents. While RAG finds relevant text, GraphRAG understands how concepts, clauses, and requirements relate—catching dependencies that keyword search misses.
Why It Matters: Prevents missed relationships. In legal due diligence, finds connected clauses across contracts. In systems engineering, tracks cascading requirements. In construction, maps spec cross-references.
Best For: Documents with complex interconnections, regulatory compliance, systems with dependencies
Trade-off: Higher upfront cost, but finds connections RAG misses
Related Terms: Knowledge Graph, Entity Extraction, RAG
RAPTOR / Multi-Layer Summarization
Technical Definition: Recursive Abstractive Processing for Tree-Organized Retrieval—builds hierarchical document summaries at multiple abstraction levels.
Business Translation: Automatic creation of executive summaries AND detailed views. Executives get 2-paragraph overviews, specialists get exact specifications—from the same system, same documents.
Why It Matters:
- Answers questions at the right detail level automatically
- “What’s the project scope?” → Executive summary
- “What concrete strength?” → Exact specification
- Saves 40-60% of “clarifying question” cycles
Best For: Large multi-volume documents, teams with varying information needs, complex proposals
Related Terms: Hierarchical Summarization, Abstraction Layers
Chunking
Technical Definition: Dividing documents into smaller segments for embedding and retrieval.
Business Translation: Breaking large documents into searchable pieces. Like creating an index, but smarter—preserving context and meaning across boundaries.
Why It Matters: Proper chunking preserves context. Bad chunking splits related information, leading to incomplete answers.
Typical Approaches:
- Fixed size: Every 500 words (simple but risks splitting context)
- Semantic: Natural section boundaries (preserves meaning)
- Structural: Based on document structure (headings, paragraphs)
Related Terms: RAG, Embedding, Context Boundaries
Performance & Infrastructure Terms
VRAM (GPU Memory)
Technical Definition: Video Random Access Memory—the memory on graphics processing units used for AI computation.
Business Translation: Computer memory needed to run AI models. More VRAM = ability to process longer documents or run larger models.
Why It Matters: Determines hardware costs and capabilities.
- 24GB VRAM: Handle 32K token contexts
- 80GB VRAM: Handle 128K token contexts
- 400GB+ VRAM: Handle 1M token contexts (multiple GPUs required)
Cost Impact: High VRAM GPUs are expensive. Smart retrieval (RAG/GraphRAG) reduces VRAM needs by 3-10x.
Related Terms: GPU, Context Window, On-Premise Deployment
Latency / Query Latency
Technical Definition: The time delay between submitting a query and receiving a response.
Business Translation: How fast you get answers. Sub-second latency means instant responses. 30+ second latency disrupts workflow.
Why It Matters: User adoption depends on speed. If AI search takes 30 seconds, people revert to manual methods.
Typical Performance:
- RAG: 1-3 seconds (fast retrieval + AI generation)
- GraphRAG: 2-5 seconds (graph traversal + AI generation)
- Full-document processing: 10-60+ seconds (expensive and slow)
Related Terms: Throughput, Performance, Response Time
Quantization
Technical Definition: Reducing the numerical precision of model parameters (e.g., from 16-bit to 8-bit or 4-bit) to reduce memory and computation requirements.
Business Translation: Making AI models smaller and cheaper to run with minimal quality loss. Like compressing a photo—smaller file size, nearly identical appearance.
Why It Matters:
- 4-bit quantization: 4x memory reduction (run on smaller/cheaper GPUs)
- 1-3% quality reduction (acceptable for most business applications)
- Dramatically lower infrastructure costs
Trade-off: Slight accuracy reduction for significant cost savings
Related Terms: VRAM, Model Optimization, Inference Efficiency
Problem Areas & Solutions
Attention Dilution
Technical Definition: The phenomenon where transformer models struggle to focus on relevant information when context windows become very large.
Business Translation: The “too much information” problem. When AI tries to read 500,000 words at once, it struggles to find the important parts—like asking someone to remember every detail from 10 books simultaneously.
Why It Matters: Bigger context windows don’t always mean better results. Smart retrieval focuses AI attention on what matters.
Related Terms: Lost in the Middle, Context Window, RAG
Lost in the Middle
Technical Definition: Research-validated phenomenon where LLMs have lower accuracy for information located in the middle of long contexts.
Business Translation: AI struggles to “remember” information buried deep in long documents. Like skimming a 500-page report—you remember the beginning and end better than the middle.
Why It Matters: Even with large context windows, brute-force approaches miss critical information. Retrieval strategies surface the right information regardless of location.
Related Terms: Attention Dilution, Context Window
Hallucination
Technical Definition: When language models generate plausible-sounding but factually incorrect information.
Business Translation: AI making things up. Without grounding in your actual documents, AI might invent contract clauses or specifications that don’t exist.
Why It Matters: Critical for high-stakes applications (legal, construction, compliance). RAG/GraphRAG reduce hallucinations by 90%+ by grounding responses in actual document content.
Prevention: Citation-based approaches, retrieval-augmented generation, human verification for critical decisions
Related Terms: Grounding, Citation, RAG
Deployment & Integration Terms
On-Premise / Self-Hosted
Technical Definition: Deploying AI infrastructure on your own servers rather than using cloud services.
Business Translation: Running AI on your own computers/servers instead of using vendor APIs. Complete data control, but requires hardware and IT management.
Why It Matters:
- Pros: Complete data control, no data leaves your network, predictable costs at scale
- Cons: Upfront hardware costs ($30K-500K+), IT overhead, slower deployment
Best For: Highly sensitive data, high query volumes, regulatory requirements, air-gapped environments
Related Terms: Cloud Deployment, Hybrid Deployment, Air-Gapped
API (Cloud Deployment)
Technical Definition: Application Programming Interface—accessing AI services over the internet via vendor-provided endpoints.
Business Translation: Using AI as a service (like Netflix vs. owning DVDs). Pay per use, no hardware required, instant access to latest models.
Why It Matters:
- Pros: No infrastructure, instant scaling, latest models, low setup cost
- Cons: Data sent to vendor, variable costs, internet dependency
Best For: Moderate workloads, flexible requirements, faster deployment, non-sensitive data
Common Providers: OpenAI, Anthropic, Google, Cohere
Related Terms: Cloud Deployment, On-Premise, Hybrid
Hybrid Deployment
Technical Definition: Combining on-premise and cloud infrastructure, routing workloads based on sensitivity and requirements.
Business Translation: Best of both worlds—sensitive documents processed on-premise, routine queries via cloud APIs. Optimizes for security, cost, and performance.
Why It Matters: Flexibility without compromise. Process confidential contracts locally, use cloud for routine specs searches.
Example Architecture:
- Sensitive legal documents: On-premise processing
- Public specifications: Cloud APIs (cheaper, faster)
- Dynamic routing based on document classification
Related Terms: On-Premise, Cloud Deployment, API
Architecture Types
Transformer
Technical Definition: The dominant neural network architecture for language models, using self-attention mechanisms to process sequences.
Business Translation: The standard AI architecture (used by GPT-4, Claude, Gemini). Works great for typical documents but becomes expensive for very large contexts.
Why It Matters: Industry standard with proven quality, but quadratic cost scaling means 10x larger context = 100x higher processing cost.
Best For: Documents under 100,000 tokens, proven quality requirements, access to latest commercial models
Related Terms: Attention, Context Window, LLM
State Space Models (Mamba, RWKV)
Technical Definition: Alternative neural architectures with linear-time complexity, designed for efficient long-sequence processing.
Business Translation: Newer AI architectures optimized for massive documents. 3-5x faster and 50-70% cheaper than transformers for 100K+ token contexts.
Why It Matters: Makes previously impractical workloads affordable. Process 1M token documents that would be prohibitively expensive with transformers.
Best For: Very large documents (>100K tokens), high query volumes, on-premise deployments with limited hardware
Trade-off: Less mature than transformers, fewer commercial options, best for specialized workloads
Related Terms: Transformer, Linear Complexity, Long-Context Processing
Business Terminology
ROI (Return on Investment)
What It Means: How much value you get compared to what you spend.
For AI Document Processing:
- Investment: Implementation cost ($25K-500K) + monthly operational cost ($500-5K/month)
- Return: Time savings (50-80% reduction in search time), faster projects, lower risk exposure
- Typical Payback: 3-6 months for teams of 5+ people
Calculation Example:
- 10 people saving 15 hours/week at $100/hour = $15K/week savings
- Annual savings: $780K/year
- Implementation cost: $100K
- Payback period: 6-7 weeks
Related Terms: Payback Period, TCO, Cost-Benefit Analysis
TCO (Total Cost of Ownership)
What It Means: All costs over the solution’s lifetime, not just upfront price.
Components:
- Upfront: Implementation, integration, training
- Recurring: API costs, infrastructure, support/maintenance
- Hidden: Staff time for management, updates, troubleshooting
Why It Matters: A “cheap” solution with high ongoing costs can be more expensive than a higher upfront investment with low operational costs.
Comparison:
- Cloud API approach: Low upfront ($25K-50K), higher recurring ($2K-5K/month)
- On-premise approach: High upfront ($100K-200K), lower recurring ($500-1K/month)
- Break-even: Typically 12-24 months depending on usage volume
Related Terms: ROI, Payback Period, Operational Costs
SLA (Service Level Agreement)
What It Means: Guaranteed performance and availability commitments.
Typical AI Service SLAs:
- Uptime: 99.9% availability (less than 9 hours downtime/year)
- Response time: 95% of queries under 3 seconds
- Support response: 4-24 hours depending on tier
- Data security: Encryption, backup, compliance standards
Why It Matters: For production systems, you need guaranteed performance. SLAs provide recourse if service degrades.
Related Terms: Uptime, Performance Guarantees, Support Tiers
Quick Reference: Common Conversions
Tokens to Words:
- 1,000 tokens ≈ 750 words
- 10,000 tokens ≈ 7,500 words (10-15 pages)
- 100,000 tokens ≈ 75,000 words (100-150 pages)
- 1,000,000 tokens ≈ 750,000 words (1,000-1,500 pages)
Context Window Comparisons:
- 8K tokens: Short article or email thread
- 32K tokens: Medium document (30-50 pages)
- 128K tokens: Long document (150-200 pages)
- 1M tokens: Very long document set (1,000-1,500 pages)
- Enterprise document sets: Often 2M-10M+ tokens
Cost Scaling (Approximate):
- 10K context: Baseline ($0.01-0.05 per query)
- 100K context: 10-20x baseline
- 1M context: 100-200x baseline
- RAG approach: 70% cost reduction vs. full-context
Still Confused?
This glossary covers the most common terms. If you encounter jargon we haven’t explained:
For Business Buyers: Contact us and we’ll explain in plain English how it affects your decision.
For Technical Teams: See our Technical Specifications for deeper architectural details.
For Everyone: Our FAQ addresses common questions about implementation, costs, and capabilities.
Related Resources: