Kuldeep Paul

LLM costs have become a critical concern for engineering teams deploying AI applications at scale. In 2025, API pricing ranges from $0.25 to $15 per million input tokens and $1.25 to $75 per million output tokens, creating significant budget variability depending on model selection and usage patterns. Organizations

TLDR: Long-context LLMs often miss information placed mid-sequence (“lost in the middle”), driven by positional biases like RoPE decay. Production fixes: two-stage retrieval (broad recall + cross-encoder reranking), hybrid search (semantic + BM25), and strategic ordering (top evidence at start and end). Strengthen chunking with contextual retrieval; keep only the most relevant

Modern AI applications (chatbots, copilots, RAG systems, and voice agents) live and die by the quality of their evaluations. If you cannot trust your evals, you cannot trust your releases. The most reliable way to achieve trustworthy AI evaluation is to curate a high-quality “golden dataset” that mirrors production reality,

Multi-agent systems have become the standard architecture for complex AI applications. However, as these systems grow more sophisticated, understanding their behavior in production becomes increasingly challenging. Without proper observability and evaluation, teams face issues ranging from unexpected agent handoffs to degraded response quality, problems that only surface after deployment. This

Google’s Agent Development Kit (ADK) makes it straightforward to design multi‑agent systems, while Maxim provides the end‑to‑end stack for simulation, evaluation, and observability required to ship these systems reliably. This guide shows how to combine ADK and Maxim for robust agent tracing, debugging, and continuous quality