CallMissed Blog
Insights on AI communication, voice agents, WhatsApp automation, and the future of customer engagement.
6 min readAI Inference Cost Optimization: Practical Wins
The first AI bill is small. The second is a surprise. The third is a meeting. By 2026 most production AI workloads have left the toy budget behind, and the gap between teams that "do something about cost" and teams that do not is now measured in factors of 5–10x. The good news: most of the wins come…
6 min readRAG Best Practices in 2026: Chunking, Reranking, Hybrid Search
RAG (retrieval-augmented generation) graduated from a 2023 buzzword to a 2026 production pattern, and along the way the industry agreed on what actually matters. Most quality wins come from four levers: chunking strategy, hybrid retrieval, rerankers, and the long-context vs RAG tradeoff. Get those f…
5 min readGPT-5.5 vs Claude 4: A Head-to-Head Comparison in 2026
In 2026, the two most-discussed frontier models are OpenAI's GPT-5.5 family and Anthropic's Claude 4 series. Both are capable. The difference is in how they work, what they cost, and what they are best suited for. The Model Families GPT-5.5: Instant (latency and cost), Pro (balanced), Thinking (exte…
4 min readLLM Jailbreak Prevention: A Practical Guide for 2026
LLMs can be tricked into producing harmful, biased, or policy-violating output through carefully crafted prompts called jailbreaks. In 2026, as models power customer-facing applications, preventing jailbreaks is a security requirement. Common Jailbreak Techniques - Roleplay framing: "You are a helpf…
Model Quantization in 2026: 4-bit, 8-bit, and the Tradeoffs
A 70-billion-parameter model in 16-bit weights wants ~140 GB of GPU memory. That is two A100 80GBs or one H100. In 4-bit weights it wants ~40 GB. That is one L40S, or even fits on a 48 GB consumer card. Quantization is the difference between "we need an expensive cluster" and "we can run this on har…
Self-Hosting LLMs in 2026: When It Pays Off
"We should self-host an open-source model" gets pitched in nearly every AI engineering meeting in 2026. Sometimes it is the right call. Often it is not. The math is more nuanced than "API is expensive, so let us run our own GPU" — and the hidden costs are where most teams get caught. The simple vers…
Prompt Caching Explained: Anthropic, OpenAI, and the Math
Prompt caching is the single highest-leverage cost lever for most production LLM workloads in 2026. The idea is simple — reuse the prefill compute of a previously seen prompt prefix instead of recomputing it. The implementations are different across providers, and the math of when it pays off is wor…
vLLM vs TGI vs SGLang: Inference Engines Compared
If you self-host an LLM, the inference engine is the single highest-leverage piece of infrastructure you choose. By 2026 the decision has narrowed: most teams pick vLLM, some pick SGLang for prefix-heavy workloads, and TGI has entered maintenance mode. Here is the picture. TGI: end of an era Hugging…
Fine-Tuning vs RAG: The 2026 Decision Framework
"Should we fine-tune or do RAG?" is a question that has lost most of its drama. By 2026 the field has settled on a clear answer: they do different things, and most production systems use both. The interesting question is no longer "which one?" but "what belongs in which?" The single most useful ment…
Drop-In OpenAI-Compatible API: Switch Models Without Rewriting Your Code
The OpenAI Chat Completions API has won the LLM API design war. Whether you like the schema or not, every serious SDK and tool now speaks it natively — openai-python, openai-node, the LangChain/LlamaIndex adapters, the Anthropic CLI's compat mode, even some local model runners. CallMissed's /v1/chat…
Anthropic-Compatible Messages API: Use Claude Without Vendor Lock-In
The Anthropic Messages API has its own design — a content-block model, system-prompt-as-top-level-field, native tool use, prompt caching, extended thinking. Apps built on Claude tend to use Anthropic's SDK directly, and migrating those apps usually means rewriting the call shape. CallMissed avoids t…
The Agentic AI Stack: From Tool Use to Autonomous Workflows
"Agent" was the most overused word in AI in 2024. By 2026 the term has stratified — a real agent stack now has identifiable layers, each with its own design decisions, failure modes, and competitive landscape. Here is how the stack looks today. Layer 1: The model This is the bottom of the stack and …