The ChuksForge Blog
Technical articles on LLM engineering, multi-agent systems, AI deployment in Africa, and enterprise automation — from the engineers who build these systems daily.
The LLM Cost Curve Nobody Models: Retry Storms
A cost model built from token counts and expected call volume misses the multiplier that actually blows up a bill: naive retry logic on a flaky model call, quietly multiplying spend every time the underlying provider has a bad afternoon.
How to Do Bookkeeping on WhatsApp (Step-by-Step)
No app to download, no forms to fill — just chat. Here's exactly how to record sales, expenses, customer debt, and check your numbers on WhatsApp, walked through step by step with BOS Assistant.
Best Bookkeeping Apps for Nigerian SMEs in 2026
Kippa, Wave, and myBooks compared honestly for Nigerian SMEs — and why a growing number of business owners are logging sales by chatting on WhatsApp instead of opening a bookkeeping app at all.
What Systematic Trading Taught Me About Trusting AI Output
A systematic trading background changes how you look at a model's confidence score. It's a backtest result, not a guarantee — and the same discipline that keeps a trading strategy honest is what keeps an AI system honest in production.
Best Kippa Alternatives in 2026
Outgrown Kippa, or just want to see what else is out there? Here's an honest look at the real alternatives — including one that isn't really "switching apps" at all.
Best LLM Evaluation Tools in 2026
Braintrust, LangSmith, and Langfuse are the names that come up first for LLM evaluation. Here's where each actually fits — and the specific gap none of them close.
How to Stop Losing Money to Bad Recordkeeping
It rarely feels like recordkeeping is the problem. It feels like the business just isn't as profitable as it should be. Often those are the same problem, wearing a different name.
Promptfoo vs evalci-core: Open-Source LLM Eval Comparison
Both open-source. Both run in CI. Genuinely different tools underneath — plus one recent development worth factoring in if vendor neutrality matters for your evals.
Idempotency Keys Aren't Optional for AI Agents That Take Actions
An agent that only answers questions can retry freely. An agent that charges a card, sends a message, or books a slot can't — not without an idempotency key. Here's the pattern and where it breaks if you skip it.
How to Track Customer Debt and Credit Sales in Nigeria
Running a tab for regular customers is normal in Nigerian retail. Losing track of who owes what isn't inevitable — here's how to actually keep on top of it.
Introducing EvalCI: A Self-Hosted CI Gate for LLM Quality
We've written before about why evals belong in CI, not a one-off notebook. EvalCI is the tool we built to make that the default instead of the exception — self-hosted, with an explicit baseline and an Apache-2.0 core.
How to Add LLM Regression Testing to Your CI/CD Pipeline
Not "run some evals occasionally" — actually gating a merge on eval results, the same way a broken unit test blocks one. Here's the concrete setup with EvalCI.
Building AI Systems for Low-Connectivity Markets: Lessons from Nigeria
Most AI infrastructure is designed by people with stable broadband, testing on the same. Building for Nigerian SMEs — where connectivity drops mid-conversation and data cost is real, not abstract — forces decisions that make a system better everywhere.
Best Inventory Management Apps for Nigerian SMEs (2026)
Barcode scanners and POS terminals solve inventory for a supermarket. They're overkill for a stall that just needs to know when rice is running low. Here's what actually fits which kind of Nigerian business.
Multi-Tenancy Patterns for AI SaaS: Row-Level Security First
Multi-tenancy decisions made in week one are some of the hardest to reverse later. Here's the RLS-first pattern we use for AI products built on Clerk and Supabase, and the mistakes it's designed to prevent.
Why Your LLM Eval Scores Don't Match Production Behavior
The eval suite is green. Production complaints are up. Both of those things can be true at once — here's why, and what actually closes the gap.
How to Calculate Profit Margin as a Nigerian Retailer
Sales were good this month — but were you actually profitable? A plain-language walkthrough of calculating real profit margin, and the two mistakes that quietly inflate it for most small retailers.
What Building for Regulators-Adjacent Clients Taught Us About AI Audit Trails
A client doesn't have to be regulated itself for its AI system to need a real audit trail. Once a client operates anywhere near a compliance-sensitive space, "the model said so" stops being an acceptable answer to "why did the system do that", and the architecture has to account for it from day one.
Feature Flags for Prompts: Shipping LLM Changes Without a Full Deploy
We've written about versioning and testing prompts like code. The next piece is shipping them like code too — percentage rollout, per-tenant override, and an instant kill switch, instead of a full deploy for every prompt tweak.
Prompt Engineering Isn't Engineering Until You Version and Test It
A prompt that lives in a chat window and hasn't been tested against a real dataset isn't a system component — it's a guess with good formatting. Here's the practical difference.
Setting Up Golden Datasets for LLM Regression Testing
A regression suite is only as good as the dataset behind it. Here's how to actually structure one — sized, scoped, and organized so it catches real regressions instead of noise.
Why "Just Add RAG" Is Usually the Wrong Answer to a Hallucination Problem
A model confidently states something false, and the reflexive fix is "add retrieval so it has real information to work from." Often that's treating the wrong layer — the failure is frequently in grounding or instruction-following, not in a knowledge gap retrieval can fill.
Why Most Multi-Agent Systems Fail Before They Reach Production
Multi-agent demos are everywhere. Multi-agent systems running reliably in production are rare. The gap usually comes down to three decisions teams make in week one and never revisit.
Debugging a Distributed Trace Through Three LLM Calls and a Human Handoff
A real incident, walked end to end: an alert fires, the root cause is three LLM calls and a human handoff deep, and the only reason it took twenty minutes instead of two days is that every hop was instrumented before it needed to be.
Prisma 7 + Next.js 16: The Migration Gotchas Nobody Documents Yet
Upgrading to Prisma 7 and Next.js 16 looks straightforward from the changelogs. In practice, three quiet breaking changes will each cost you an afternoon if you don't know they're coming.
Why We Run LLM Evals in CI Like Tests, Not as a One-Off Notebook
A notebook that scored well once tells you nothing about the prompt change you're about to ship. Evals only matter if they run on every change, automatically, the same way your unit tests do.
AI Unit Economics: How to Actually Attribute LLM Cost to a Customer or Feature
"Our AI costs are up" is not actionable. Which customer, which feature, which model call — that's the question finance will actually ask, and most teams can't answer it.
Multi-Agent Orchestration Patterns: When You Need a Supervisor (and When You Don't)
Every team building with LLMs eventually asks: do I need multiple agents, or one agent with more tools? The answer is almost always simpler than the architecture diagrams suggest.
Why LLM Observability Is Non-Negotiable Once You Are in Production
You wouldn't run a backend service in production without APM. Most teams ship LLM features with zero visibility into cost, latency distribution, or failure modes. Here is what to instrument and why it matters before it becomes an incident.
WhatsApp Business API for Nigerian MSMEs: What Nobody Tells You Before You Start Building
The WhatsApp Business API looks simple in Meta's documentation. Shipping it reliably for a Nigerian MSME, on Nigerian networks, under Nigerian usage patterns, is a different problem. Here is what actually matters.
Building Production-Grade RAG Systems: The Architecture Decisions That Actually Matter
Most RAG tutorials stop at a demo that embeds a PDF and retrieves three chunks. Production RAG is a systems problem, not a prompt problem — here is what actually breaks at scale, and how to design around it.