How a hybrid approach can drastically reduce operational costs while maintaining conversational quality
Modern conversational AI systems face a critical challenge: providing intelligent, natural responses while keeping operational costs sustainable at scale. Large Language Models (LLMs) offer unprecedented conversational capabilities, but their token-based pricing makes them expensive for high-volume applications.
General Bots addresses this challenge with a unique hybrid approach that intelligently routes conversations through multiple processing modes, utilizing the most cost-effective method for each interaction while maintaining quality.
The most cost-efficient approach uses pre-defined question/answer pairs stored in tabular files. When a user query closely matches a known question, the system can immediately return the corresponding answer without invoking any complex processing.
Cost: Nearly zero (simple string matching)
Use case: FAQs, common queries, predictable interactions
When direct matching fails, General Bots employs search techniques similar to Elasticsearch to identify the most relevant information from its knowledge base. This approach uses keyword extraction, semantic similarity, and ranking algorithms to find the best possible answers without requiring LLM processing.
Cost: Very low (search computation only)
Use case: Information retrieval, knowledge base queries, documentation search
For more complex queries that require understanding intent but not creative generation, General Bots employs traditional Natural Language Processing techniques. This includes intent classification, entity extraction, sentiment analysis, and rule-based response generation.
Cost: Low (on-premise computation)
Use case: Intent-based routing, form filling, structured queries
When all other modes prove insufficient, General Bots escalates to LLM processing for the most complex, nuanced, or creative interactions. This ensures users still receive high-quality responses for queries that truly require advanced AI capabilities, while reserving LLM usage for only when necessary.
Cost: Highest (token-based pricing)
Use case: Creative content, complex reasoning, nuanced interactions
When a user query enters the system, the orchestrator quickly evaluates which processing mode is most appropriate:
The cost benefits of General Bots' hybrid approach become evident at scale. For a typical enterprise deployment handling 100,000 interactions per month:
$950/month
100% of queries processed by LLM
$350/month
Only 35% of queries require LLM
$150/month
Only 15% of queries require LLM
With proper knowledge base management and continuous optimization of decision rules, organizations can push even more interactions to lower-cost processing modes, further reducing operational costs while maintaining high response quality.
Success with General Bots depends heavily on well-structured knowledge. Invest time in creating comprehensive question/answer pairs and organizing information in ways that facilitate efficient search and retrieval.
Develop clear rules for when to escalate between processing modes. Consider factors like query complexity, confidence scores from each mode, and business priority of different interaction types.
Implement feedback loops that capture successful and failed interactions, using this data to improve your knowledge base and refine decision rules for mode selection.
General Bots' hybrid approach represents a significant advance in making conversational AI economically viable at enterprise scale. By intelligently routing conversations through multiple processing modes based on complexity, organizations can achieve the perfect balance between cost efficiency and conversational intelligence.
As AI costs continue to be a limiting factor for wide-scale adoption, solutions like General Bots that optimize for both performance and cost will become increasingly valuable in the conversational AI landscape.
General Bots® LLM and custom AI models.
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