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ChatGPT 5 vs Claude Sonnet: AI Coding Skills Compared What is the future of coding? It is no
so people keep talking about prompts like that's the whole game right?
just write a better prompt
just tell the model what you want
just give it more context?
but after building agents and chatbots for a while, you start seeing the same ugly pattern again and again
the problem is usually not the model itself, let's be honest we don't need AGI for most things
it's the mess around it, and that mess is context
not just the prompt
the whole environment around the model
and when more instructions are added, the model starts behaving weirdly and stops following what you want
that's where things stop being cute demos and start becoming real engineering
at first i thought prompting was the hard part and that was the hype back when models weren't that good and we saw AI as just a fancy autocomplete
finding the right wording, the right tone, the right examples, the right output format and adapting it to the task, the use case, and the user
but honestly that part is easy compared to what comes after
because in a real product you rarely have one clean instruction
you have a stack
and all of this lands in front of the model like one giant mixed soup
that's where the real headache starts
because now the question is not just “what should the assistant do?”
it becomes “which instruction should win?”
and that sounds simple until you actually build something with tools, memory, multiple flows, edge cases, retries, fallbacks, different personas, different user states, temporary rules, safety rules, formatting rules, product rules...
then suddenly one tiny sentence placed in the wrong layer can make the whole thing act weird. in the end it's not the model's fault, it's the context's fault
this is the part i think many people underestimate
instruction placement matters almost as much as instruction quality maybe even more
you can write a very smart instruction and still get bad behavior if you put it in the wrong place
for example
you want a global tone rule that probably belongs high in the hierarchy
you want a specific formatting rule only when calling one tool that should probably stay local to that tool
you want temporary behavior only for this conversation that should not live in a permanent memory layer
you want a safety boundary that should not be casually mixed with product fluff
but when you're building fast, especially in early versions, everything gets thrown together
and the model still answers, so you think it's fine. until it's not
until one user asks something weird or memory injects something old let's say the logic we write is 100% bug-free or retrieval brings in text that sounds authoritative or a tool instruction quietly overrides the thing you thought was “the main rule”
and now the assistant is technically working but behaviorally unstable
that kind of bug is annoying because it doesn't look like a bug at first
it looks like inconsistency or randomness or "models are weird lol"
sometimes yes but a lot of the time it's just bad context design
one thing that hit me pretty hard while building agents is this:
models do not really experience your architecture the way you do
you see neat boxes
system memory tools history policy user input
the model just sees tokens
sure, some layers carry stronger priority depending on the platform and setup but inside the actual generation process, it is still trying to resolve a pile of instructions and signals
so if your context is noisy, repetitive, conflicting, vague, or badly scoped, the model does not become “more informed”
it becomes less stable
this is why throwing more context at the problem often makes things worse
!! some models are built to handle big context mess so... !!
people love saying context is king yeah, sure but bad context is a corrupt king
more history is not always better more retrieval is not always better more memory is definitely not always better
especially in long chats, you should not keep dumping the entire thread back into the model forever
if the user keeps sending messages in the same conversation, you need to decide what still matters:
sometimes the thing making your assistant look dumb is not lack of intelligence
it's lack of signal clarity. the better the tokens you pass to the LLM, the better the output usually comes out.
the important instruction is there but buried under six other things fighting for attention
and this leads to another painful truth
you need to design for conflict
because conflict will happen whether you planned for it or not
you might have one instruction saying: be concise
another saying: explain clearly for beginners
another saying: always mention edge cases
another saying: format as json
another saying: be friendly and natural
another saying: never ask follow-ups unless needed
and all of them sound reasonable alone
together? not always
so what happens?
the model tries to satisfy all of them halfway
and halfway compliance is where mediocre outputs are born
not because the model is stupid unless u are using one but because the system asked for five different personalities at once
this is why context engineering is not just about adding information
it's about reducing contradiction
deciding what is permanent what is scoped what is optional what is strongest what is fallback what must survive compression what can be dropped when token pressure increases
that's real work
when i was building chatbots earlier, i used to think memory was the magic part
like yeah, just give it memory and now it becomes smart
not really
memory is dangerous too
because memory can help continuity but memory can also drag old assumptions into a new conversation where they no longer belong
and once that starts happening, the assistant feels strange
too sticky too biased by old context too confident in something that should have been temporary
same thing with retrieval
people talk about retrieval like it automatically upgrades intelligence
it can but it can also inject irrelevant stuff at the worst possible moment
and the model has no human feeling of “this source is probably noise” unless you design the whole pipeline carefully
ranking matters selection matters truncation matters summarization matters even the order of inserted context matters
small decisions, huge behavior difference
honestly the more i build this stuff, the less i think of AI apps as “prompt engineering”
that term feels too small now
because i'm not really engineering a prompt
i'm engineering attention
what should be visible? what should be loud? what should be quiet? what should persist? what should disappear? what should override? what should never override?
that is the real job, honestly
and that's why context engineering matters so much
it is the layer that decides whether the model feels sharp or sloppy
same model, same temperature, same tools
different context design => completely different product
and maybe that's the funniest part
when context engineering is done well, people think the model is amazing
when it is done badly, people blame the model
but many times the model did exactly what the environment pushed it to do
it followed the wrong thing, at the wrong time, because the system made the wrong thing feel important
so yeah, after working on agents and chat systems, i've become way less obsessed with the model alone
the model matters, of course.
but context is what turns raw capability into useful behavior
that's the difference between something that answers and something that actually feels reliable
and once you see that, you stop asking only
what model should i use?
you start asking better questions
where should this instruction live?
what should have the highest priority?
what context is helping?
what context is just noise?
what survives when the conversation gets long?
what breaks when user behavior gets messy?
that's where the real game starts
so no, context engineering is not just some fancy new term
it's probably one of the most practical shifts in how we build AI products
because sooner or later every serious chatbot becomes less about generation
and more about control
and context is the control layer