Full transcript
0:00Over
0:01the last few years, we've seen an
0:03incredible jump in AI capabilities.
0:06Models are larger, context window are
0:08longer, and benchmarks keep getting
0:10broken. If you look only at demos, it
0:13feels like we're very close to real
0:15intelligence. And yet, if you talk to
0:18engineers building AI systems in uh
0:21production, a very different picture
0:22emerges. System that looked impressive
0:26in testing becomes unreliable at scale.
0:29answers sound confident but are wrong.
0:31Edge cases dominate real uses and
0:34debugging becomes guesswork. This gap
0:38between what we see in demos and what
0:40survives in production is what this
0:42series is about. Not new models, not new
0:46frameworks, but why AI system fails even
0:49when the models are excellent.
0:52We have good models than ever. And yet
0:54AI systems are becoming harder to trust,
0:57not easier.
0:59This sounds counterintuitive, but it's
1:02observable everywhere. Like I said,
1:04demos look impressive. Product videos
1:07look magical. Early prototypes feel
1:09intelligent. But once this system hit
1:13real users, they hallucinate
1:15confidently. They mix irrelevant context
1:17with relevant facts. They fail silently
1:20and they give answer that sounds right
1:22but are fundamentally wrong.
1:25And all of this isn't speculation.
1:29Microsoft research has shown that
1:31retrieval augmented systems often
1:33degrade model performance when retrieval
1:36quality is slightly off. And instead of
1:38saying I don't know, the model
1:40confidently blends noise into the
1:42answer.
1:43OpenAI's GPT4 technical report
1:46explicitly notes that fluency and
1:48confidence increase faster than factual
1:51reliability, making human evaluator
1:53harder. not easier.
1:56And Enthropic has repeatedly highlighted
1:59that longer context windows increase the
2:01risk of contradiction, stale
2:03information, and mispress confidence
2:06when context is not carefully
2:08controlled.
2:09In other words, the models improved, but
2:12the system didn't.
2:15And when these failures happen, the
2:17response is almost always the same.
2:20Let's improve the prompt or we'll add
2:22more tools or we have to add some memory
2:26or we'll add an agent. But here's the
2:29uncomfortable truth. More prompts don't
2:32fix the architectural mistakes. More
2:34tools don't create control and more
2:36chains don't create understanding. They
2:39often make the system look smarter while
2:42becoming harder to reason about, harder
2:44to debug, and harder to trust.
2:47Now this series is not about the hype.
2:50It's not about making AI look
2:52impressive. It's about understanding why
2:54system fails. Even when every individual
2:57component looks powerful but because
3:01until we face that honestly no amount of
3:04better models will say badly designed
3:06systems and that is where AI systems
3:09engineering begins.
3:13Now that we've seen the illusion,
3:15impressive demos, confident
3:17hallucinations, and silent features,
3:19let's talk about why this happens at a
3:21deeper level. The problem isn't just
3:24noisy retrieval, flaky memory, or
3:26missing feedback loop. The problem is
3:28the mindset, especially the LLMcentric
3:31trap.
3:33Many teams treat the language model as
3:35the brain of the system. If the model
3:37can generate text, it must be able to
3:39reason, plan, remember, and answer
3:42truthfully all at once. It is an
3:45understandable assumption. After all,
3:47these models are quite impressive, and
3:49they often look intelligent. But here's
3:51the true reality. Intelligence is not
3:54text generation.
3:56Producing plausible text is not the same
3:58as understanding, reasoning, or making
4:01choices. And expecting one component to
4:04reliably do all of these is a recipe for
4:07a brittle system.
4:09You've probably seen these signs in your
4:12own experiments. The model confidently
4:15gives incorrect answers, yet the rest of
4:17the system blindly trusts them. Planning
4:20or multi-step reasoning fails silently.
4:24The steps look right, so the conclusion
4:26is wrong. or memory or context
4:29accumulates irrelevant information over
4:31time polluting outputs or the system
4:34breaks in unpredictable ways under scale
4:37or edge cases even if it work in small
4:40test.
4:42These are the classical
4:44signals that the architecture is trying
4:46to make the model do too much.
4:50Let's be clear, LLM are powerful tools.
4:53They excel at synthesizing language,
4:55pattern completion, generating coherent
4:57outputs, but they're not inherent
5:00planners, evaluators or truth engines.
5:05They do not know what they don't know.
5:07They do not manage feedback loop and
5:09they do not separate reasoning from
5:11action. And when you treat them as the
5:14brain rather than a component,
5:15everything else in the system,
5:17retrieval, memory, planning, evaluation
5:19becomes fragile and small mistakes
5:22scarred.
5:23confidence misleads and the system
5:25eventually collapses.
5:28In the next part, we'll see how rag, the
5:31so-called seral bullet, also falls into
5:33this trap. Retrieval, chunking, and
5:36context management all fail silently if
5:38the system architecture assumes that the
5:40model will just get it right. And
5:43understanding this is the first step
5:45towards thinking in systems, not just
5:47component.
5:50So we've seen why treating the LM as the
5:53brain of the system is a trap. Now we'll
5:56talk about the more popular mitigation
5:58retrieval augmented generation or rag.
6:01And at first glance it seems perfect.
6:04Retrieve retrieve relevant information
6:06from a knowledge base feed it to the
6:08model and let it generate accurate
6:10answers. On paper it looks like we've
6:13solved hallucinations, memory gaps and
6:14long-term knowledge. But here is the
6:16real truth. Rag is powerful but when it
6:20is designed as a system it is not a
6:23feature you can just drop into a
6:25pipeline and then expect magic.
6:28Rack doesn't magically make your model
6:30intelligent. It promises you one thing.
6:33Given the right context the model can
6:35generate answers grounded in retrieve
6:37information. That's it. It does not
6:40guarantee relevance. It does not
6:41guarantee comprehension. It does not
6:43prevent overconfidence. and it does not
6:45automatically correct mistakes in the
6:47retrieval process.
6:50Most drag pipelines
6:53failures happen quietly. You might not
6:56notice them until the system is live.
6:58And here's what typically goes wrong.
7:01The first chunking is not the same as
7:04understanding. Breaking document in
7:06small pieces doesn't mean the model
7:08understands them. Chunk boundaries
7:10misplate important context. Summaries
7:13can omit
7:14critical nuances and so on. Second,
7:17retrieval is not equal to relevance. A
7:20retrieve document might contain partial
7:22answers, outdated info or noise. If the
7:25model trusted blindly, the answer can be
7:28confidently wrong. Microsoft's research
7:31rag studies shows that retrieval errors
7:33often amplify hallucinations rather than
7:36reducing them. And third, context is not
7:39equal to knowledge. Even with perfect
7:42retrieval, feeding text to the model
7:44doesn't guarantee it will reason
7:46correctly or remember facts
7:47consistently.
7:49Long context can dilute focus.
7:51Contradictory passages can confuse the
7:54model.
7:55So rag is not a magic bullet. It is a
7:58system that requires architecture and
8:00not a single component. You need to have
8:04clear separation between retrieval,
8:06reasoning and validation. You need to
8:08have guardrails for noisy or irrelevant
8:11data. You also need strategies to manage
8:14long-term context without overloading
8:16the model. And you need continuous
8:18evaluation of what the model actually
8:20knows versus what it assumes.
8:23Without this system thinking, rack
8:26pipelines often look like they're
8:27working until they silently fail in
8:30production.
8:32Now in the next section we'll see that
8:34agents another popular solution that we
8:37assume also amplify the same problem.
8:39Retrieval tools planning none of these
8:42fix the root issue if the architecture
8:44treats the model as a brain and
8:47understanding these limitations is the
8:49key to thinking like an AI systems
8:51engineer rather than just a prompt
8:53engineer.
8:56So if rag is not a silver bullet, one
9:00might think maybe agents are the answer,
9:02right? After all, agents promise
9:05autonomy, planning, reasoning, and tool
9:07use all wrapped into one system. And
9:10they sound like intelligence made
9:11tangible. But here's the reality. Agent
9:15do not fix bad architecture. They
9:17amplify it.
9:20So agent gives the illusion of
9:21intelligence. They can choose tools,
9:24plan multiple steps and respond
9:26dynamically. But what happens when the
9:29underlying system is already fragile?
9:32Every small architectural flaw, missing
9:35validation, noisy retrieval, weak memory
9:38management now actually starts to
9:41propagate faster. The system looks more
9:44autonomous but it's more chaotic.
9:47And many agent system fall into what I
9:51call tool chaos.
9:53tools are called unnecessarily. Outputs
9:56are ignored or misused and planning
9:59steps conflict with each other and the
10:01result the agent appears smart in demos
10:04but in real world production it makes
10:07unpredictable untraceable choices and
10:10the autonomy you see is mostly illusion
10:12not true system intelligence.
10:16Agents often operate stoastically. Their
10:19actions may change
10:22certainly between runs for developers
10:25expecting repeatable results. That is a
10:28nightmare. And true intelligence in a
10:30system isn't stoastic behavior. It's
10:33deterministic control over information
10:35flow, reasoning, and actions.
10:38Agents without architecture blur this
10:40line, making debugging, evaluation, and
10:43reliability almost impossible.
10:46Here are the key takeaways.
10:49First, agents are powerful only when
10:51they sit inside a welldesigned system.
10:53Second, they do not replace pipelines,
10:56evaluation loops, or separation of
10:58responsibilities. And third, handling a
11:01model or set of tools without boundaries
11:03is like giving a toddler a Swiss army
11:05knife. It might work sometime, but the
11:07consequences of failures are
11:08unpredictable.
11:10Microsoft and OpenAI's internal
11:12evaluations on autonomous agent experime
11:14experiments shows that failure modes
11:18scale with system complexity not with
11:20model complexity.
11:24So far we've seen that the LLMs are not
11:27brains. Rag pipelines silently fail
11:30without proper design and agents amplify
11:32flaws rather than fixing them. So the
11:35root problem is missing system thinking.
11:38There's no separation of
11:40responsibilities, no controlled
11:41information flow, no proper evaluation
11:44and no guardrails for uncertainty. And
11:46this is exactly what AI systems
11:49engineering is meant to solve.
11:54So if we step back from all the hype LLM
11:57rag agents, one thing becomes clear.
12:00These failures that we talked about
12:02aren't random. They aren't because the
12:04model is weak. The failure happens
12:06because we never treated AI as a system.
12:09In most AI present projects, a single
12:13component often the LLM is expected to
12:15do everything.
12:17Reasoning, memory, planning, truth,
12:19validation, toolation, everything. This
12:22lack of separation of responsibilities
12:24creates fragile systems. When one piece
12:27fails, the entire system collapses
12:29silently.
12:32Without explicit design around how data
12:35moves through the system, information
12:37leaks, noise and irrelevant context
12:40pollute the outputs. Retrieval results,
12:43cache memory and model predictions
12:46interact in unpredictable ways. Small
12:49mistakes cascade and your smart system
12:51BFS irrationally under pressure.
12:55With absence of feedback loops, most AI
12:58pipeline don't actually learn from their
13:00own mistakes.
13:02Wrong answers aren't flagged. Misuse
13:04tools are uncorrected and context
13:06pollution isn't pruned.
13:10Without feedback loops, error accumulate
13:12silently and system reliability degrades
13:15over time.
13:18And we engineers often evaluate AI
13:20output subjectively. It sounds right.
13:23But without evaluation boundaries,
13:25explicit metrics for correctness,
13:27confidence, and relevance, systems
13:30cannot be trusted in production. The
13:32model will always be persuasive even
13:35when it's wrong.
13:38And a subtle but critical error is
13:40replacing lodging with
13:44text generation just because an LLM can
13:46do it. So instead of uh deterministic
13:50rules or structured reasoning, we ask a
13:52stoastic model to enforce correctness.
13:55And this creates a brittle systems where
13:58reasoning fails silently and
13:59unpredictably. And this is exactly where
14:02AI systems engineering comes in. It's a
14:05mind shift. It's a mindset shift from
14:09what library should I use to what
14:11architecture solves this problem
14:12reliably.
14:14We build systems with clear
14:16responsibilities, control information
14:18flow, feedback loop, and evaluation
14:20boundaries. We use LLMs for what they
14:23excel at, language synthesis, and build
14:26everything else explicitly.
14:28In short, we engineer intelligence
14:31instead of hoping the model will provide
14:33it.
14:37So, so far we've looked at why AI
14:40systems fail. We've seen the traps LLM
14:42treated as brain rack pipeline failing
14:44silently agent amplifying chaos and
14:47absence of system thinking. But this
14:50series isn't just about the problems
14:52like I've been talking about. It's also
14:54about the solutions, the way senior
14:56engineers think when building AI systems
14:58that actually work. And the answer is
15:01thinking in pipelines, not prompts. You
15:04learn to uh design pipelines, not just
15:07promps here. Instead of asking how do I
15:10get this model to answer correctly, we
15:12will be asking questions like how should
15:14information flow through the system?
15:16Which components are responsible for
15:18retrieval, reasoning and evaluation?
15:20This mindset will separate reliable AI
15:23system from fragile experiments.
15:27We'll also teach you how to build
15:30systems that anticipate failure, systems
15:32that degrade gracefully, that fail
15:35predatively, and that provide meaningful
15:37feedbacks when things go wrong. Because
15:40failure isn't something to avoid. It's
15:42the system telling you where the design
15:43is weak.
15:45You'll also learn how to make AI output
15:47transparent and explainable. Not just
15:50output that looks good, but outputs you
15:52can reason about, trust, and debug.
15:56And uh explanability isn't optional.
15:59It's essential for real world
16:01application. You'll also gain intuition
16:04about what models can and cannot do and
16:07when to rely on an LLM, when to validate
16:09output, and when to insert uh
16:13deterministic logic. The goal here is
16:16practical grounded intelligence, not
16:18overconfidence in a component that looks
16:20smart.
16:22And above all, we'll adopt the mindset
16:24of engineering intelligence. We build
16:26architecture first. Then we validate
16:29ideas with minimal code. Only after the
16:32system is sound do we scale or optimize.
16:35And this is what separates hobbyist
16:38prompt experiments from production grade
16:40AI systems.
16:43And by the end of this series, you won't
16:45just know how to use models. you'll know
16:48how to build AI system that are
16:49reliable, explainable, and robust. And
16:53that is the foundation we're trying to
16:55build in AI systems engineering here.
17:01So before we wrap up, here's something I
17:04want you to take away. If your AI system
17:06fails, it's trying to tell you
17:08something. Every hallucination, every
17:10silent fe failure, every confusing
17:13output is not just a bug. It's a signal,
17:16a reflection of architectural weakness,
17:18missing controls, or unbalanced
17:20responsibilities. And this series is
17:22about listening to these signals, not
17:24covering them up with more prompts, more
17:26tools, or more complex agents. We're not
17:29here to chase hype. We're here to
17:31understand, diagnose, and engineer AI
17:33systems that actually work in the real
17:35world. And that requires thinking in
17:37pipelines, designing for failure, and
17:40separating reasoning from action. It's
17:43about seeing the system as a whole, not
17:45just a model that generates
17:49text.
17:51So in the next episode, we'll answer a
17:54simple but fundamental question. What is
17:56an AI system? Really, we'll define
17:59intelligence at the system level, not
18:01the model level. And we'll see why
18:03understanding this identification is the
18:06foundation for building reliable,
18:08scalable, and production ready systems.
18:11So take a moment after this episode,
18:13reflect on the AI systems you've built
18:15or interacted with up until now. Listen
18:18to what their failures are telling you
18:20and get ready because in the next
18:21episode we start thinking about AI the
18:25way a systems engineer thinks, not just
18:27a model user. I'll see you in the next
18:30episode.