Model & LLM Behavior

Overview

The Large Language Model (LLM) is the brain of your RAG system—it takes retrieved context and generates natural language responses. Even with perfect data integration, chunking, and retrieval, LLM configuration and behavior can make or break the user experience. Understanding and controlling LLM behavior is critical for reliable, accurate, and safe AI agents.

Why LLM Behavior Matters

Proper LLM configuration ensures:

• Grounded responses - Answers based on retrieved context, not fabricated

• Consistent quality - Predictable behavior across conversations

• Appropriate tone - Responses match your brand and use case

• Safe outputs - Protection against prompt injection and misuse

• Cost efficiency - Optimal model selection and token usage

Poor LLM management leads to:

• Hallucinations - Model generates plausible-sounding but incorrect information

• Context overflow - Important information lost when context exceeds limits

• Inconsistent responses - Same question gets different answers

• Refusal to answer - Over-cautious model declines valid queries

• Security vulnerabilities - Prompt injection attacks bypass controls

Common LLM Challenges

Response Quality

• Hallucination despite retrieved context - Model ignores facts and invents answers

• Response inconsistency - Different answers to the same question

• Incorrect citation format - Poor source attribution

• Language mixing - Unintended language switching in responses

Configuration Issues

• Temperature setting problems - Too high (random) or too low (repetitive)

• Token limits exceeded - Context too large for model

• Context window overflow - Critical information pushed out

• Model switching mid-conversation - Inconsistent behavior across turns

Safety & Security

• Prompt injection attacks - Users manipulate system prompts

• Refusal to answer - Over-cautious filtering rejects valid questions

• Sensitive information leakage - Model reveals confidential data

Solutions in This Section

Browse these guides to optimize LLM behavior:

• Hallucination in Responses

• Context Window Overflow

• Token Limit Exceeded

• Temperature Setting Issues

• Prompt Injection Attacks

• Model Switching Mid-Conversation

• Response Inconsistency

• Refusal to Answer

• Incorrect Citation Format

• Language Mixing in Responses

Model Selection Guide

Different models for different needs:

Model Category	Examples	Best For	Watch Out For
Premium	GPT-4, Claude 3 Opus	Complex reasoning, high accuracy	Cost, latency
Balanced	GPT-3.5, Claude 3 Sonnet	General purpose, good cost/performance	May hallucinate more
Fast	Claude 3 Haiku	High-volume, simple queries	Reduced reasoning capability
Open Source	Llama 3, Mistral	Data privacy, cost control	Need infrastructure, tuning
Domain-specific	Med-PaLM, BloombergGPT	Specialized accuracy	Limited outside domain

Selection criteria:

• Accuracy requirements - How critical are errors?

• Response latency - How fast do responses need to be?

• Cost constraints - What's your budget per query?

• Data privacy - Can data leave your infrastructure?

• Reasoning complexity - How sophisticated are the queries?

Best Practices

Prompt Engineering

1. Clear system prompts - Define role, behavior, and constraints explicitly

2. Grounding instructions - "Only use information from the provided context"

3. Citation requirements - "Always cite sources using [doc_id] format"

4. Handling uncertainty - "Say 'I don't know' if context doesn't contain the answer"

5. Tone and style - Specify formality, technicality, and personality

Context Management

1. Prioritize information - Most relevant context first

2. Stay within limits - Monitor token usage, truncate if needed

3. Summarize when necessary - Condense long contexts intelligently

4. Include metadata - Source, date, relevance scores help grounding

Temperature & Sampling

1. Low temperature (0.0-0.3) for factual Q&A, deterministic responses

2. Medium temperature (0.4-0.7) for balanced creativity and consistency

3. High temperature (0.8-1.0) for creative tasks, brainstorming

4. Top-p sampling - Consider nucleus sampling for controlled creativity

Quality Assurance

1. Test with edge cases - Ambiguous queries, missing context, adversarial inputs

2. Monitor hallucination rates - Track groundedness in retrieved context

3. Validate citations - Ensure quoted content actually exists in sources

4. A/B test prompts - Compare system prompt variations on real queries

Security

1. Detect prompt injection - Look for instruction-like patterns in user input

2. Separate user vs system instructions - Clear boundaries in prompts

3. Output filtering - Check for PII, sensitive data leakage

4. Rate limiting - Prevent abuse and cost overruns

Impact on User Experience

LLM behavior directly shapes user perception:

Behavior	User Perception	Business Impact
Hallucination	"This tool lies"	Loss of trust, abandonment
Refusal to answer	"This is useless"	Frustration, low adoption
Inconsistency	"It's unreliable"	Confusion, reduced usage
Slow responses	"It's too slow"	Poor UX, high bounce rate
Incorrect citations	"Can't verify answers"	Lack of confidence
Grounded, cited answers	"This is helpful and trustworthy"	Adoption, trust, value

Advanced Techniques

Retrieval-Augmented Generation Patterns

Basic RAG:

User Query → Retrieve Context → Generate Answer

Multi-stage RAG:

User Query → Query Enhancement → Retrieve Context → 
Rerank → Generate Answer → Validate Citations

Iterative RAG:

Generate Initial Answer → Check if sufficient → 
If not → Retrieve More Context → Refine Answer

Hallucination Detection

Post-process responses to check grounding:

1. Claim extraction - Parse factual claims from response

2. Evidence matching - Verify each claim against retrieved context

3. Confidence scoring - Flag low-confidence or unsupported claims

4. Auto-correction - Remove or qualify ungrounded statements

Context Optimization

Maximize effective context usage:

• Relevant snippet extraction - Pull specific sentences, not full documents

• Progressive context loading - Start small, add more if needed

• Hierarchical summarization - Multi-level context for long documents

• Query-focused summarization - Condense context to answer specific question

Response Formatting

Structure outputs for better usability:

• Markdown formatting - Headers, lists, code blocks

• Source citations - Inline references with links

• Confidence indicators - "Based on X sources" vs "I'm not certain"

• Follow-up suggestions - Proactive next questions

Quick Diagnostics

Signs your LLM configuration needs work:

• ✗ Responses contradict retrieved documents

• ✗ Same question yields different answers across sessions

• ✗ Model invents sources or citations that don't exist

• ✗ Valid questions get "I can't answer that" responses

• ✗ Responses are too verbose or too terse

• ✗ Citations are wrong or missing

• ✗ Model reveals system prompts when challenged

Signs your LLM is configured well:

• ✓ Answers are grounded in retrieved context

• ✓ Consistent responses to repeated questions

• ✓ Appropriate "I don't know" for out-of-context queries

• ✓ Citations are accurate and verifiable

• ✓ Tone and style match your brand

• ✓ Resistant to prompt injection attempts

• ✓ Fast, cost-effective responses

Monitoring & Metrics

Track these metrics for LLM health:

Quality Metrics

• Hallucination rate - % of claims not supported by context

• Citation accuracy - % of citations that point to correct content

• Response consistency - Similarity of answers to duplicate questions

• User satisfaction - Thumbs up/down, star ratings

Performance Metrics

• Response latency - Time to first token, total generation time

• Token usage - Input tokens, output tokens, cost per query

• Context utilization - % of provided context actually used

• Model API reliability - Uptime, error rates

Safety Metrics

• Prompt injection attempts - Detected adversarial inputs

• Refusal rate - % of queries declined (should be low but non-zero)

• PII leakage incidents - Sensitive data in responses

• Policy violations - Outputs against content policy

Bottom line: The LLM is your AI agent's voice. Configure it carefully, test thoroughly, and monitor continuously to ensure it represents your brand and serves your users effectively.