Practical perspectives on building, deploying, and refining AI-driven dialogue systems — written for practitioners, not spectators.
How the open-source model wave changed what self-hosting actually costs
A close look at how the open-source model wave changed what it actually costs to build and run a conversational AI system in 2024.
A cost-focused comparison of two dominant approaches to domain knowledge in AI systems
An analytical breakdown of when retrieval-augmented generation saves money compared to fine-tuning, with specific cost markers to watch.
How prompt structure affects your API bill more than most teams realize
Poorly structured prompts waste tokens and inflate API bills. Here is how recent prompt design patterns reduce inference costs without degrading response quality.
Volume thresholds, hidden pricing, and the self-hosted alternative
Platforms like Voiceflow, Botpress, and Flowise promise faster builds. A look at where that speed translates to real savings and where hidden costs appear.
How tiered model selection reduces inference spend without degrading user experience
Not every conversational query needs a large model. Task-specific routing to smaller, cheaper models has become a practical cost strategy in 2024.
Cheaper evaluation tooling and what it means for development budgets
Teams spend more on testing and evaluating their conversational AI systems than they expect. Recent tooling changes have made this significantly cheaper.
Each piece on this page addresses a specific decision point in conversational AI development — choosing between retrieval-augmented generation and fine-tuning, handling multi-turn context degradation, or structuring intent hierarchies for production chatbots.
Cerevyny has been working in this space since 2017. The writing here reflects accumulated experience from real client systems, not survey-based summaries.
Practical depth across the full conversational AI stack — from architecture to deployment.
State machines, flow graphs, and turn management for complex multi-step conversations.
Vector stores, chunking strategies, and embedding selection for grounded AI responses.
When to fine-tune versus prompt-engineer, and how to prepare domain-specific datasets.
Metrics that matter for dialogue quality — beyond BLEU scores and perplexity.
Output filtering, jailbreak resistance, and responsible deployment patterns.
Connecting conversational agents to CRMs, APIs, and internal data sources reliably.