AWS Certified Generative AI Developer - Professional: RAG debugging: hallucinations, cost, latency

Day 16 rag debugging hallucinations cost

latency. Day 16 is where AWS stops

asking whether you can build rag and

starts asking whether you can debug it

under pressure. This day is not about

adding features. It's about diagnosing

what is broken and fixing the right

layer. Imagine this. An offshore oil rig

deploys an AI safety assistant.

It's trained on emergency procedures,

equipment manuals, and safety

checklists. Engineers complain it

sometimes makes things up. It's too slow

during emergencies. It's costing a

fortune. Management concludes the model

is broken. But the model is not broken.

The pipeline is. Let's start with the

most important problem, hallucinations.

Here is the single most important exam

rule for day 16. If a RAG system

hallucinates, the problem is almost

never the LLM. Hallucinations come from

the system around the model. There are

three main causes. First, bad retrieval.

This is the most common failure. The

model sounds confident, but the answer

is wrong. It mixes procedures. It

mentions facts that are not in the

documents. This happens because

retrieval is noisy or incomplete.

Chunking may be poor. Overlap may be too

small. Top K may be wrong. Metadata

filters may be missing. Hybrid search

may not be used. The correct fixes are

always retrieval fixes. Improve

chunking. Increase overlap. Add metadata

filters. Use hybrid search. Add

rerankers. The wrong fixes are classic

traps. Using a smarter model, increasing

temperature, fine-tuning the model.

Those do not fix hallucinations caused

by bad retrieval. Second cause, missing

grounding rules. Sometimes retrieval is

fine, but the model answers even when

context is missing. It fills the gap

with general knowledge. That is not

intelligence. That is a lack of

constraints. The fix is grounding. You

explicitly tell the model answer only

using the provided context. If the

answer is not in the context, say I

don't know. On the exam, this is called

grounding. Third cause, no answer

validation. Even with good retrieval and

grounding, models can still add small

unsupported claims or draw conclusions

not backed by evidence. Answer

validation is the final safety net. The

system checks. Is every claim supported

by the retrieve text? If not, the answer

is rejected or revised. If AWS mentions

unsupported claims or extra facts after

rag, the fix is answer validation. Here

is the exam shortcut. When you see

hallucinations, think retrieval first,

then grounding, then validation, never

bigger model. Now, let's debug cost. Rag

systems get expensive quietly. Cost

comes from embeddings, vector storage,

input tokens, output tokens, and model

choice. The biggest cost problem is

usually too many tokens. Large retrieved

context, high top K, verbose system

prompts. The fix is not tuning. The fix

is discipline. Reduce top K. Remove

irrelevant chunks. Shorten prompts. Use

re-rankers to keep only the best

evidence. Choose smaller models where

possible. Another cost trap is using the

wrong model. If you use Claude Opus to

rewrite boilerplate text, you are

burning money. For bulk text, use Titan

text light or express. For short, fast

answers, use Haiku. Use Sonnet only when

accuracy truly matters. AWS loves cost

aware model selection. A third cost

issue is re-mbedding everything. If

documents haven't changed, embedding

them again is wasted money. The correct

approach is embed once, re-mbed only

when documents change. Version your

data. If the exam mentions spiking

embedding costs, this is the fix. Now,

let's debug latency. Latency matters

most in emergency systems, voice

assistance, and real-time chat. The

first latency bottleneck is retrieval.

Large indexes, no filters, high top K.

Fix this with metadata filtering, lower

top K, hybrid search, and pre-filtering.

The second latency bottleneck is the

model. Large models, long prompts, no

streaming. The fixes are simple. Use

streaming responses. Use smaller models

when possible. Reduce context size. AWS

expects you to know that streaming

reduces time to first token. The third

latency issue is overengineering. Query

rewriting, multi-query, re-rankers all

improve accuracy and all add latency.

The senior move is not to remove them.

The senior move is to use them only when

needed. Cache results and apply them

selectively to hard queries. Accuracy

layers cost time. Use them deliberately.

Now lock in the debugging mindset. Ask

one question first. Is the answer

factually wrong? Fix retrieval. Is the

answer confident but unsupported? Add

grounding and validation. Is the system

too expensive? Reduce tokens and model

size. Is the system too slow? Optimize

retrieval, streaming, and model choice.

Is the system inconsistent? Then, and

only then, consider fine-tuning.

Avoid the classic traps. Fine-tuning

does not reduce hallucinations. Bigger

models do not fix retrieval. Higher

temperature does not improve accuracy.

Guardrails do not fix wrong facts. Every

fix belongs to a specific layer. Here is

the one sentence that solves day 16.

Hallucinations come from bad retrieval.

Cost comes from bad token discipline.

Latency comes from bad architecture

choices. Say that once before the exam.

Final self test. A rag system retrieves

correct documents, but answers include

extra facts not present in the context.

What should you add? Grounding rules and

answer validation. That's day 16

mastered.