Context Window Optimization with Binary Search

Introduction

In the world of large language models (LLMs), the Context Window is a finite and extremely valuable resource. To ensure AI always operates at peak performance without overflow errors or losing important details, Prisma AI has implemented Binary Search technique to optimize information allocation.

1. The Challenge of Token Limits

Every AI model has a maximum limit on the number of Tokens (text units) it can process in a single query.

Problems When Sending Too Much

AI gets "overwhelmed" with data
Incorrect or inaccurate responses
System errors due to exceeding limits

Problems When Sending Too Little

AI lacks necessary context
Incomplete answers
Missing important details

┌─────────────────────────────────────────────────────────┐
│                   TOKEN LIMIT CHALLENGE                 │
├─────────────────────────────────────────────────────────┤
│                                                         │
│   Too many tokens          Too few tokens              │
│   ┌─────────────┐          ┌─────────────┐             │
│   │ ████████████│          │ ██          │             │
│   │ ████████████│          │             │             │
│   │ ████████████│          │             │             │
│   │ ██ OVERFLOW │          │  MISSING    │             │
│   └─────────────┘          └─────────────┘             │
│   ❌ System error          ❌ Missing context          │
│                                                         │
│                    "Sweet spot"                         │
│                  ┌─────────────┐                        │
│                  │ ████████    │                        │
│                  │ ████████    │                        │
│                  │ ████████    │                        │
│                  │  OPTIMAL    │                        │
│                  └─────────────┘                        │
│                  ✅ Optimal performance                 │
└─────────────────────────────────────────────────────────┘

2. Optimization Technique Using Binary Search

Prisma AI uses the optimize_documents_for_token_limit function combined with the Binary Search algorithm to find the "sweet spot" of input information.

Processing Workflow

Step 1: Calculate base context

The system first determines the token count of fixed components:

System Prompt
Chat History
Query Templates

Step 2: Measure document cost

Every chunk from the knowledge base is accurately token-counted using token_counter technology.

Step 3: Find optimal length

Instead of randomly cutting documents, the Binary Search algorithm will:

Continuously halve the document list
Test each portion
Precisely determine the maximum number of document chunks

Step 4: Reserve space for response

The system always proactively reserves an output buffer of approximately 2000 tokens to ensure AI has enough space to write complete answers.

Binary Search Algorithm Illustration

┌─────────────────────────────────────────────────────────┐
│              BINARY SEARCH OPTIMIZATION                 │
├─────────────────────────────────────────────────────────┤
│                                                         │
│  Documents: [D1, D2, D3, D4, D5, D6, D7, D8]           │
│  Token Limit: 8000 tokens                               │
│                                                         │
│  Iteration 1: Try all 8 docs → 12000 tokens ❌         │
│               [████████████████████████]                │
│                                                         │
│  Iteration 2: Try 4 docs → 5000 tokens ✅              │
│               [████████████]                            │
│                                                         │
│  Iteration 3: Try 6 docs → 7500 tokens ✅              │
│               [██████████████████]                      │
│                                                         │
│  Iteration 4: Try 7 docs → 8500 tokens ❌              │
│               [████████████████████████]                │
│                                                         │
│  Result: 6 documents = OPTIMAL ✅                       │
│               [██████████████████]                      │
│                                                         │
└─────────────────────────────────────────────────────────┘

Step	Documents	Tokens	Result
1	8	12,000	❌ Exceeds limit
2	4	5,000	✅ Room left
3	6	7,500	✅ Near optimal
4	7	8,500	❌ Exceeds limit
Result	6	7,500	✅ Optimal

3. Advanced Content Summarization Optimization

For extremely long documents, Prisma AI applies the optimize_content_for_context_window technique.

How It Works

AI uses binary search to:

Compress original text content to ideal length
Preserve core arguments
Stay within model processing capacity

┌─────────────────────────────────────────────────────────┐
│           CONTENT OPTIMIZATION FLOW                     │
├─────────────────────────────────────────────────────────┤
│                                                         │
│  Original Document (50,000 tokens)                      │
│  ┌─────────────────────────────────────────────────┐   │
│  │ ████████████████████████████████████████████████│   │
│  └─────────────────────────────────────────────────┘   │
│                         │                               │
│                         ▼                               │
│              Binary Search Optimization                 │
│                         │                               │
│                         ▼                               │
│  Optimized Content (8,000 tokens)                       │
│  ┌─────────────────┐                                   │
│  │ ████████████████│ ← Core arguments preserved        │
│  └─────────────────┘                                   │
│                                                         │
└─────────────────────────────────────────────────────────┘

4. Results: Accurate Information, Stable Performance

Thanks to intelligent token management, Prisma AI delivers outstanding benefits:

Eliminate Overflow Errors

Ensures 100% of queries are executed successfully, no more errors from exceeding token limits.

Prioritize Important Information

Documents with highest relevance (after Rerank) are always prioritized for inclusion in the context window first.

Cost Savings

Only send the right amount of data, optimizing API budget for enterprises.

Benefit	Description
100% Reliability	No more token overflow errors
High Quality	Important information prioritized
Optimized Cost	Only use necessary tokens
Complete Responses	Always buffer for output