跟讀練習: Top 5 AI Gateway Use Cases | Solo.io - 通過影片學習英語口語

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Continuing on the theme of AI related connectivity, let's talk about AI gateways.

A really common question we get is, why do I need an AI gateway?

And maybe I'll just build one myself or use my existing API gateway for that functionality.

So let's examine the top five use cases we see in customers adopting an AI gateway.

The first one involves credential management.

So without an AI gateway, a very common pattern you see is I'll create a key here in my LLM provider,

and then I'll go ahead and take that key and distribute it to the application teams,

the teams writing agents, and anyone maybe that they're using an agentic IDE and they need an API key there as well, and you can distribute it in all those locations.

Functionally this works.

The problem is when any one of these developers that has seen

that key maybe leaves the company or accidentally checks it into a public GitHub repository,

the blast radius of that is this entire infrastructure, including your account in the ALM provider.

One thing that an AI gateway helps out with immediately is

that I can have safe and secure storage for my API keys that only my AI gateway has access to.

The AI gateway then can, if we want, go ahead and issue individual keys for specific use cases for specific user groups.

Or I can also integrate with existing authentication mechanisms I have in my organization.

Like if an agent is presenting a JWT, I can use that and map it to the API key I'm providing to the provider.

This is critical just in terms of the blast radius limitation of like, hey, a key is exfiltrated,

or someone has hacked a certain piece of code and gained access to a certain piece of code.

I know exactly how to contain that threat.

And by containing it, I'm not impacting any of the use cases I have for that LLM provider.

I would say that credential management also plays a key role in the other use cases we're about to talk about now.

So let's talk about rate limiting.

Why is rate limiting important?

So maybe you've already seen this in your existing applications where let's say you have,

this app is sending five requests with a token size of let's just say 20K tokens, right?

And over here, this IDE is sending 20 requests with a token size, like a 45k token window,

like every minute or so, right?

Now you end up with turning an agent on and the agent goes bananas.

And all of a sudden it sends, you know, 20,000 requests and a massive context window that it's dumping in.

And it's got like a million tokens, let's say, just in a very short period of time, like in less than a minute.

Now in

that scenario, what's going to happen is the LLM provider is going to rate limit not only based on request count, but also by token count.

So those are both variables in that equation and evaluation.

And when you get shut off, you're going to get shut off, not just for the use case that's abusing it, but account wide, and you're going to get a backoff.

That means maybe this agent, I'm just testing around or it's an evaluation,

but I brought my entire account down and that's all API keys for that account across all the other use cases.

It's impacting developers, it's impacting my production apps that may be customer facing as well.

We have to find a better way to deal with this problem.

And the answer is with an AI gateway to actually support rate limiting

and token rate and rate limiting in particular directly in the gateway.

And that means when I have this same traffic here where I'm coming over and this is the bad actor.

I have our IDE friends over here.

This agent, since it's interacting with the AI gateway first,

the AI gateway can shut off that agent directly while allowing traffic from all other consumers to continue.

That means I have no production or service interruptions from the app or the IDE, and the agent gets really gentle back pressure

and gets turned off for a period of time and then gets opened up again.

It'll be very obvious to that team that they're getting rate limited locally, and then they can fix that problem and address it and come back online.

Or maybe it was a periodic or ephemeral issue with flooding a bunch of traffic, and as soon as they exceed the rate limiting window, then they'll come back online.

But the key thing in any gateway and why API gateways, traditional API gateways are not sufficient to handle this requirement is you need both request rate limiting,

but also token weighted rate limiting.

So I can compute the amount of tokens that are allowed given a certain time window in seconds, minutes, hours or days.

All right.

Use case number three, usage reporting.

So maybe this is familiar to you as you're consuming more and more of provider services.

end of the day you get a nice bill from your provide your LLM provider

and it's a million dollars okay okay who has actually spending

that money okay you may be lucky enough to have multiple API keys

so you get maybe some subdivision of that but again

if you're sharing API keys across the clients

that are using it then you're gonna that's gonna get washed

out more importantly you're not gonna have the very specific details of the identities the groups that are consuming it, the model and the provider used in

that internal detail of how you're actually going to show chargeback or showback to the teams consuming.

So what we want is the ability that we'll get the same million dollar bill over here.

Okay.

But in this scenario, every request that's going through the AI gateway is actually earmarked

or watermarked with the client actually consuming it.

That's how we're doing rate limiting.

And that's why we implement credential management in this layer as well, which means that when I get this $1 million bill,

the access logging and reporting here can actually tell me that this particular app is actually responsible for 50K

and this IDE is responsible for 50K.

Or in like, you know, the token count, it'll report the token counts and you can extrapolate the value there.

And then our friend, the agent that was acting up in terms of rate limiting, their token contribution was around 900K, right?

This is a very, very key attribute to like once you're trying to justify

that AI budget and who's consuming you're trying to even

if you're not charging back those teams you're trying to show

who's consuming consumption reporting in the gateway is a key feature

you'd certainly don't want to you don't have the right details in a traditional API management solution

or API gateway to basically track providers and clients

and token counts to show that charge back

but then also you won't have the granularity to isolate it to given teams all right number four for guardrails.

So for guardrails, what we're going to protect against is a number of things.

The request that's going in, this can be a hijack request, it could be a phishing prompt, it can be anything.

This is your attack surface area of what's going in via natural language prompt.

And they're going to be a common attack vectors that you're going to want to protect against.

Certain prompts that if they contain keywords or they're structured in a certain way, you always want to prevent.

Or perhaps you want to append a system prompt to every request that goes through.

So you can use some semantic guardrails there in line with the request.

Likewise, even if it wasn't a malicious request on the response path, I always have a danger here of data exfiltration, right?

So if I'm asking a very innocent question that's not a phishing attack or trying to be a Trojan horse, I still might get back some customer data,

account information, social security number, this type of thing that I don't want to expose to clients.

And in that case, I have no way of protecting against either of these.

The only thing I can rely on is the actual LLM providers' moderation services, which are definitely valuable but on the request path, I'm never limited.

Like any data can go all the way to the LLM provider here.

So that's actually leaving my network and is out on like open internet and available to the provider.

And I might not want that data even escaping my network, right?

Likewise, there could be very specific requirements I have where maybe the obvious things like credit card numbers and social security numbers,

you can pattern match on those and the provider services can filter those.

But what if I have very unique constraints of what I consider PII for my customer base, or I have intellectual property that I don't want to escape my network,

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I don't want developers accidentally putting in key bits of code

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or other IP materials that actually might throw to an LLM provider, that I need to be able to instrument for advanced guardrails as well.

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So let's look at how an API or an AI gateway solves for that.

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In the AI gateway, you actually have three controls available.

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So you have basic guardrails.

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And what basic guardrails do is the first use case I said, that acting directly on the prompt and the response, it can introspect the request, it can identify keywords, identify patterns,

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and reject those and reject that request to not allow it to flow through to the upstream LLM provider.

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Likewise, in the response path, it can inspect the data and it can either reject

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that outright or it can mask that data as it comes back to the client.

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The key value there is that I don't have to implement it all of the client apps, these guardrails.

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I have a centralized point of control.

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This becomes very important if after going to production, you discover that some key data is being exfiltrated, or you're open to some attack vector.

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What is the fastest path to remediating that exposure?

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And it's not rolling out new updates to all the apps you've deployed, and it's not working with your upstream LLM provider to add that to their moderation endpoint.

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that's actually implementing that in the AI gateway and getting it pushed to production in minutes.

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The next feature I'll mention is moderation endpoint integration.

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So again, this is a key feature that the upstream LLM providers all offer, but why would I want to wait necessarily for the full request to go to the LLM

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and then catch that in the processing path?

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What I really want to do is inside my network, basically contact the moderation point, and if there's any problem with that request,

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I want that to actually be cut off in my network and not go farther downstream.

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A key principle here is trying to eliminate any threats or any potential data exfiltration as soon as you can detect it.

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And this is a key aspect of that.

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And then finally, we support what we call advanced guardrails.

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And really what that is, as an example, is if you wanted to train a small language model, maybe you take like an 8 billion parameter model

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and you fine-tune it with maybe 500 examples of various types of data

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that you don't want to see in a request or different types of prompts that you don't want to see,

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that can be very, very useful to execute inline in the request path as more of a semantic guardrail.

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So you're not even matching on specific data patterns or data signatures.

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you're doing semantic controls of, is this suggestive of this, or is this like these other things?

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And that can be done with a small language model that can be integrated directly with the AI gateway.

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The last point I want to make is around abstraction.

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So in this case, we have individual apps, agents, and IDEs going directly against the LLM provider.

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The impact of that means that they are binding to the very specific API of that provider and also they are actually,

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if you look behind the scenes, most customers, while they may be consuming the public endpoint,

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the generalized endpoint for OpenAI, for example, that we all use when we use it, but it's quite likely for sensitive information or for their enterprise use cases,

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you're consuming a private endpoint there.

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So you've isolated that LLM in specific infrastructure for your company, and you need to access that endpoint or a specific model.

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And in that scenario, now the app is going to go to that specific endpoint.

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There are definitely cases where there can be endpoint availability issues

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there for a very specific endpoint where it's unavailable for a certain period of time.

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Now in that case, if that's tied to a production experience, so like a chat bot or any customer facing functionality,

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then that customer channel is down in that period.

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Another potential exposure here is if you get rate limited in a given model, or even availability of a given model is down,

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then you might want to fall back or fail over to another model.

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But it is very hard to instrument for this.

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You actually have to build that logic directly into each application agent

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and you're hoping your IDE supports it that you're using up here, but with an AI gateway we can do that seamlessly.

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If a given endpoint, private endpoint is down, the LLM will seamlessly fall over based on your configuration to a backup endpoint

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and this can cascade down to multiple end points, multiple models, and even multiple providers.

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When we talk about abstraction, if your AI gateway supports a canonical API as we do in Agent gateway,

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all of your clients can just talk OpenAI's API, and that can be mapped to any provider underneath the cover,

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whether that's Claude or Vertex or Bedrock or OpenAI itself.

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It can scale to all of these models while presenting a single canonical and consistent API to the consumers, which makes it much easier as you start to exchange models,

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whether it's hosted providers or you're starting to run LAMA or another model, open source model internally.

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you still don't impact the consumers of that because you're keeping that API abstraction the same.

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So hopefully this was useful in terms of discussing the key use cases

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and the key value we see with customers in adopting an AI gateway

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and gives you a good idea of how daunting this task is

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if you were gonna embark on the path of implementing it on your own.

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Check out Agent Gateway today.

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本課的詞彙與口語要點

這節 C1 級別的口語課以影片“Top 5 AI Gateway Use Cases”為素材。影片中反覆出現的詞有：gateway, key, provider, request, Api。這段影片共有 159 個句子、2518 個單詞可供跟讀。講話部分時長為 13:37。說話人語速較快，每分鐘約 185 個詞，會出現較多連讀和弱讀。只有 80% 的單詞屬於英語最常用的 3,000 詞，詞彙難度較高。

影片中的重點詞彙

影片中最難的 15 個單詞，附發音和釋義：

單詞	發音	釋義
token 名詞	/ˈtoʊkən/	代幣 /代幣, 籌碼 /籌碼
endpoint 名詞		端點 /端點
guardrail 名詞	/ˈɡɑɹdˌɹeɪl/	護欄 /護欄
consume 動詞	/kənˈsum/	耗費 /耗費, 消耗
moderation 名詞	/ˌmɑdəˈɹeɪʃən/	適度 /適度
credential 名詞	/kɹɪˈdɛnʃəl/	證書
upstream 形容詞	/ʌpˈstɹiːm/	上游
likewise 副詞	/ˈlaɪkˌwaɪz/	相似地, 類似地 /類似地
keyword 名詞	/ˈkiː.wɜːd/	關鍵詞 /關鍵詞, 關鍵字 /關鍵字
phishing 名詞	/ˈfɪʃɪŋ/	釣魚法 /釣魚法, 釣魚欺詐 /釣魚欺詐
isolate 動詞	/ˈaɪ.sə.leɪt/	孤立
functionality 名詞		功能, 功能性
distribute 動詞	/dɪˈstɹɪbjuːt/	分配
radius 名詞	/ˈɹeɪ.di.əs/	橈骨 /橈骨
vector 名詞	/ˈvɛktɚ/	向量, 向量

影片中出現的短語動詞

單詞	發音	釋義
come back 動詞		回來 /回來, 返回
go ahead 動詞	/ˌɡoʊ əˈhɛd/	請便 /請便
go through 動詞		通過 /通過
check out 動詞		退房

影片中的語法

說話人最常用的結構，並附上影片中的原話：

結構	影片中的用法
被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的	is exfiltrated · are allowed · is actually earmarked
現在完成時 have/has + 過去分詞 — 過去發生但與現在仍有關聯的事	has seen · has hacked · you've already seen
定語從句 who / which + 從句 — 補充說明人或事物	well, which means · services, which are · consumers, which makes

需要注意的發音

說話人用了 60 次縮略和弱讀形式，例如 you're, don't, I'm。請按聽到的簡短形式來說。

“th” 音: authentication /ɔːˌθɛntɪˈkeɪʃn̩/, underneath /ˌʌndɚˈniθ/
“sh” 和 “zh” 音: moderation /ˌmɑdəˈɹeɪʃən/, abstraction /æbˈstɹæk.ʃn̩/, credential /kɹɪˈdɛnʃəl/, phishing /ˈfɪʃɪŋ/, authentication /ɔːˌθɛntɪˈkeɪʃn̩/
長單詞——注意重音位置: moderation /ˌmɑdəˈɹeɪʃən/, canonical /kəˈnɒn.ɪ.kəl/, authentication /ɔːˌθɛntɪˈkeɪʃn̩/, ephemeral /ɛˈfɛ.mə.ɹəl/, extrapolate /ɛkˈstɹæp.əˌleɪt/

中文母語者容易讀錯的音：

/v/ — 不要讀成 /w/，上齒輕觸下唇: vector /ˈvɛktɚ/, suggestive /sə(ɡ)ˈd͡ʒɛs.tɪv/, unavailable /ˌʌnəˈveɪləbəl/, subdivision /ˈsʌbdɪvɪʒən/
詞尾子音 — 要讀清楚，後面不要加母音: prompt /pɹɑmpt/, semantic /sɪˈmæntɪk/, likewise /ˈlaɪkˌwaɪz/, exfiltrate /ˈɛksfɪltɹeɪt/, keyword /ˈkiː.wɜːd/
/r/ 和 /l/ 的區別: guardrail /ˈɡɑɹdˌɹeɪl/, credential /kɹɪˈdɛnʃəl/, exfiltrate /ˈɛksfɪltɹeɪt/, centralize /ˈsɛntɹəlaɪz/, ephemeral /ɛˈfɛ.mə.ɹəl/

如何用這段影片練習

先完整聽一遍影片，不要開口，記下不認識的單詞。
先用 0.75 倍速逐句跟讀，熟練之後再回到正常速度。
錄下自己的聲音並與原聲對比，特別注意 token, endpoint, guardrail 這類單詞。

什麼是跟讀法？

跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。

影子跟讀法: 閱讀完整分步指南 →

跟讀練習: Top 5 AI Gateway Use Cases | Solo.io - 通過影片學習英語口語

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