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Engineering
Page 2 of 2
Engineering
six steps
the job
the bar
the system
the needs
smallest model
your data
your pick
A practical checklist for picking an LLM for your feature
4 min
two phases
prefill
95% compute
decode
10% compute
How LLM inference actually works: prefill vs decode
4 min
two models
primary
verifier
answer
When no single model is enough: the primary and verifier pattern
4 min
catch it first
{
"total": 40,
"colour": 1
}
schema
format
facts
reasoning
Common failure modes in LLM systems — and how to catch them
5 min
a profile
#1
one rank
follow
recall
format
Stop ranking LLMs, start profiling them
6 min
data, not commands
ignore prev…
fetched doc
tools
safe
Defending against prompt injection and jailbreaks — and reducing hallucination
7 min
about 40 lines
full
140 GB
LoRA
16 GB
QLoRA
6 GB
free GPU
Fine-tune your first model on free Colab: QLoRA in about 40 lines
4 min
70B, one GPU
FP16: 140 GB
INT4: 35 GB
A 70B model on one GPU: a practical guide to quantization
4 min
order matters
result
1 · save
publish
2 · publish
consumer
Save first, then publish: a simple rule for not losing work
5 min
the harness
model
harness
contradicts
The rule layer: deterministic guardrails around a probabilistic model
4 min
trace one request
foreground
background
message
specialist
event
domain
Tracing a request through a multi-agent system
5 min
rounding
0.3847 → 0.3861
outlier
16-value grid
What quantization actually does: precision loss and vector-space collapse
5 min
the bill
100
23
GPU is ~90%
Where LLM serving costs actually go
4 min
ΔW = BA
billions
ΔW
=
B
A
small dim
Why LoRA works: the intrinsic-dimensionality story
4 min
carries state
run test
one step
owns the file
From worker to specialist: an agent that owns a domain
4 min
first call
system
user: 2+2?
assistant: 4
user: ×3
12
200 OK
Working with LLM APIs: first calls, tokens, and structured output
6 min
pick the technique
zero-shot
few-shot
chain
Zero-shot, few-shot, or chain-of-thought: picking the right technique
5 min
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